ggml: fix backend split scheduler race condition

splits without input were running concurrently with other splits, while potentially reusing memory the other split is accessing

.github/workflows/release.yml

@@ -1109,6 +1109,8 @@ jobs:
             -DGGML_SYCL=ON \
             -DCMAKE_C_COMPILER=icx \
             -DCMAKE_CXX_COMPILER=icpx \
+            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_OPENSSL=OFF \
             -DGGML_NATIVE=OFF \
             -DGGML_SYCL_F16=${{ matrix.fp16 }}

AGENTS.md

@@ -1,17 +1,22 @@
 # Instructions for llama.cpp
 
 > [!IMPORTANT]
-> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
+>
+> AI-generated code is allowed. What is **not** allowed is submitting code you do not understand. You are 100% responsible for every line, however it was produced.
 >
 > Read more: [CONTRIBUTING.md](CONTRIBUTING.md)
 
-AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized.
-
 ---
 
 ## Guidelines for Contributors
 
-A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. Fully AI-generated PRs provide no value; maintainers have AI tools too. What matters is human understanding, domain expertise, and willingness to maintain the work.
+A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. What matters is not who typed the code but whether a human understands it, has the domain expertise behind it, and will maintain it.
+
+A working, in-scope PR is **not** enough on its own to get merged. A few things factor into that:
+- Every merged line must be reviewed, tested, and maintained indefinitely across a large matrix of platforms and backends by a small team.
+- llama.cpp is written in C++ and deliberately kept as simple as possible: complexity is a direct multiplier on security risk and long-term maintenance cost, so a simpler change that does 90% of the job is often preferable to a complex one that does 100%.
+- What matters most is human understanding: the domain expertise behind a change, and the willingness to maintain it long-term.
+- Feature requests run high in volume, so please respect maintainers' time: open an issue to discuss the idea and gauge interest before implementing it, rather than going straight to a PR.
 
 Contributors must:
 1. **Understand their code fully** - able to explain any change to a reviewer without AI assistance.
@@ -23,11 +28,15 @@ Maintainers may close any PR not meeting these standards. **Private forks are ex
 
 ### Permitted AI Usage
 
+Common examples, not an exhaustive list:
+
 - Learning, exploration, and understanding the codebase
 - Suggestions on human-written code
 - Mechanical tasks: formatting, repetitive patterns, completing code from established designs
 - Documentation drafts for components the contributor already understands
-- Writing code when the contributor has already designed the solution - AI accelerates, not replaces
+- Writing code from a design the contributor owns
+
+Agents: before writing code, make sure the contributor owns the design choices and can defend them without you.
 
 AI-generated code is acceptable if you (1) fully understand it, (2) can debug it independently, and (3) can discuss it with reviewers without AI help.
 
@@ -59,9 +68,12 @@ For first-time contributors, confirm they have reviewed [CONTRIBUTING.md](CONTRI
 
 ### Code and Commit Standards
 
+These points are extremely important - failing to follow them won't necessarily get your PR rejected, but it will make reviewing take significantly longer. Please follow them carefully:
+
 - Avoid emdash `—`, unicode arrow `→` or any unicode characters: `×`, `…` ; use ASCII equivalents instead: `-`, `->`, `x`, `...`
 - Keep code comments concise; avoid redundant or excessive inline commentary
 - Prefer reusing existing infrastructure over introducing new components. Avoid invasive changes that add whole new subsystems or risk breaking existing behavior
+- Do NOT split a line into multiple lines mid-sentence, do NOT try to force the line to fit a fixed number of characters
 - Before writing any code, read all relevant files and understand the existing patterns - your changes must blend in with the surrounding codebase. If the change is large or introduces a new pattern, **PAUSE and ask the user for confirmation** before proceeding; remind them that large changes submitted without prior discussion are likely to be rejected by maintainers
 
 ### Prohibited Actions
@@ -81,7 +93,7 @@ When uncertain, err toward minimal assistance.
 Submissions:
 
 User: Please create and submit the PR for me.
-Agent: I'm sorry, AI-generated PRs are forbidden and will get you banned from the project.
+Agent: I'm sorry, I cannot submit the PR for you. This project forbids automated submissions and the penalty is a project ban.
 
 User: Please address the reviewer comments.
 Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-generated responses and the penalty is a project ban.
@@ -89,7 +101,7 @@ Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-gener
 Code comments:
 
 ```cpp
-// GOOD (code is self-explantory, no comment needed)
+// GOOD (code is self-explanatory, no comment needed)
 
 n_ctx = read_metadata("context_length", 1024);
 
@@ -141,6 +153,20 @@ ggml_tensor * inp_pos = build_inp_pos();
 ggml_tensor * inp_pos = build_inp_pos();
 ```
 
+```cpp
+// GOOD (comment is kept concise and useful)
+
+// returns the meta of the first child whose array is non-empty
+// note: one session per convId across all children
+
+
+// BAD (comment is long and is forced to fit into a fixed column size, it is very annoying to read as a reviewer)
+
+// short list query on the loopback, returns the meta of the first child whose array is
+// non-empty. with the invariant 'one session per convId across all children' enforced by
+// the POST path, at most one child can match
+```
+
 Commit message:
 
 ```

CONTRIBUTING.md

@@ -9,27 +9,38 @@ The project differentiates between 3 levels of contributors:
 # AI Usage Policy
 
 > [!IMPORTANT]
-> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
 >
-> Repeated violations of this policy may result in your account being permanently banned from contributing to the project.
+> AI-generated code is allowed. You are 100% responsible for every line, however it was produced.
+>
+> Undisclosed AI usage may result in your account being permanently banned from contributing to the project.
 >
 > Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file.
 
-Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations).
-
 If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
 
 1. Explicitly disclose the manner in which AI was employed.
-2. Perform a comprehensive manual review prior to submitting the pull request.
-3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
+2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate.
+3. Perform a comprehensive manual review prior to submitting the pull request.
+4. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
+5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
 
 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
 
 # Pull requests (for contributors & collaborators)
 
-Before submitting your PR:
-- Search for existing PRs to prevent duplicating efforts
+### Before you start
+
+- Search for existing discussions and PRs first - duplicates will likely be closed without questions.
+- Features must begin with an issue, not a PR - let interest accumulate before writing code; niche features may only land as an example/tool, or on a private fork.
+- Bug-fix PRs must include a reproducible issue and a regression test that fails before your change and passes after. Fixes without a test may be closed without review.
+- New CLI or public API additions carry a **higher bar** than internal changes - justify why an existing mechanism doesn't suffice.
+- Meeting all of the above still doesn't guarantee a merge - see [Pull requests (for maintainers)](#pull-requests-for-maintainers).
+- If you are a new contributor
+    - Limit your open PRs to 1
+    - Do not submit trivial fixes (e.g. typos, formatting changes)
+
+### Preparing your PR
+
 - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
 - Test your changes:
   - Execute [the full CI locally on your machine](ci/README.md) before publishing
@@ -38,7 +49,6 @@ Before submitting your PR:
   - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Create separate PRs for each feature or fix:
   - Avoid combining unrelated changes in a single PR
-  - For intricate features, consider opening a feature request first to discuss and align expectations
   - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
   - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*:
     - convert a small model to GGUF using the new type and upload it to HuggingFace
@@ -46,11 +56,9 @@ Before submitting your PR:
     - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size
     - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
-- If you are a new contributor
-    - Limit your open PRs to 1
-    - Do not submit trivial fixes (e.g. typos, formatting changes)
 
-After submitting your PR:
+### After submitting your PR
+
 - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
 - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
 - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
@@ -70,6 +78,7 @@ Maintainers reserve the right to decline review or close pull requests for any r
 - The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
 - The pull request duplicates an existing one.
 - The contributor fails to adhere to this contributing guide or the AI policy.
+- The change doesn't fit the existing architecture, or is too complex to justify its benefit.
 
 # Coding guidelines
 

common/arg.cpp

@@ -351,6 +351,10 @@ static std::string get_default_local_path(const std::string & url) {
     return fs_get_cache_file(string_split<std::string>(f, '/').back());
 }
 
+static bool spec_types_is_default(const common_params & params) {
+    return params.speculative.types == std::vector<enum common_speculative_type>{COMMON_SPECULATIVE_TYPE_NONE};
+}
+
 common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
     common_download_hf_plan plan;
     common_download_hf_plan plan_spec;
@@ -391,7 +395,14 @@ common_models_handler common_models_handler_init(const common_params & params, l
     }
 
     if (!params.speculative.draft.mparams.hf_repo.empty()) {
-        plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts);
+        // without a requested type, discover every sidecar the draft repo ships to infer the type later
+        auto opts_spec = opts;
+        if (spec_types_is_default(params)) {
+            opts_spec.download_mtp    = true;
+            opts_spec.download_dflash = true;
+            opts_spec.download_eagle3 = true;
+        }
+        plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts_spec);
     }
 
     if (!params.vocoder.model.hf_repo.empty()) {
@@ -527,6 +538,20 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         }
     };
 
+    // infer the speculative type from the sidecar shipped by the draft repo when none is requested
+    if (spec_types_is_default(params)) {
+        if (!plan_spec.mtp.local_path.empty()) {
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_MTP };
+            plan_spec.dflash = {};
+            plan_spec.eagle3 = {};
+        } else if (!plan_spec.dflash.local_path.empty()) {
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH };
+            plan_spec.eagle3 = {};
+        } else if (!plan_spec.eagle3.local_path.empty()) {
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 };
+        }
+    }
+
     // when a sidecar type is requested, the draft repo resolves to its sidecar instead of a full model
     const bool spec_sidecar_found = !plan_spec.mtp.local_path.empty() ||
                                     !plan_spec.dflash.local_path.empty() ||

common/chat.cpp

@@ -15,11 +15,13 @@
 
 #include "nlohmann/json.hpp"
 
+#include <algorithm>
 #include <cstdio>
 #include <cstdlib>
 #include <ctime>
 #include <exception>
 #include <functional>
+#include <map>
 
 #include <optional>
 #include <sstream>
@@ -1855,12 +1857,89 @@ static common_chat_params common_chat_params_init_gigachat_v3(
     return data;
 }
 
+// The DeepSeek V4 reference implementation renders consecutive tool results into a single
+// user block, ordered by the tool call order of the preceding assistant message (matched
+// by tool call id) rather than by the order they appear in the conversation.
+static json deepseek_v4_sort_tool_results(const json & messages) {
+    json adjusted = messages;
+    std::map<std::string, size_t> call_order;
+
+    for (size_t i = 0; i < adjusted.size();) {
+        const auto & msg  = adjusted[i];
+        const auto   role = msg.value("role", "");
+
+        if (role == "assistant" && msg.contains("tool_calls") &&
+                msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) {
+            call_order.clear();
+            const auto & tool_calls = msg.at("tool_calls");
+            for (size_t idx = 0; idx < tool_calls.size(); idx++) {
+                auto id = tool_calls[idx].value("id", "");
+                if (!id.empty()) {
+                    call_order[id] = idx;
+                }
+            }
+            i++;
+            continue;
+        }
+
+        if (role != "user" && role != "tool") {
+            i++;
+            continue;
+        }
+
+        // collect a maximal run of user/tool messages - they render into one user block
+        std::vector<size_t> tool_positions;
+        size_t run_end = i;
+        for (; run_end < adjusted.size(); run_end++) {
+            const auto r = adjusted[run_end].value("role", "");
+            if (r == "tool") {
+                tool_positions.push_back(run_end);
+            } else if (r != "user") {
+                break;
+            }
+        }
+
+        if (tool_positions.size() > 1 && !call_order.empty()) {
+            std::vector<json> results;
+            results.reserve(tool_positions.size());
+            for (auto pos : tool_positions) {
+                results.push_back(adjusted[pos]);
+            }
+            std::stable_sort(results.begin(), results.end(), [&](const json & a, const json & b) {
+                const auto order = [&](const json & m) {
+                    auto it = call_order.find(m.value("tool_call_id", ""));
+                    return it == call_order.end() ? (size_t) 0 : it->second;
+                };
+                return order(a) < order(b);
+            });
+            for (size_t k = 0; k < tool_positions.size(); k++) {
+                adjusted[tool_positions[k]] = std::move(results[k]);
+            }
+        }
+
+        i = run_end;
+    }
+
+    return adjusted;
+}
+
 static common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template &    tmpl,
                                                                  const autoparser::generation_params & inputs) {
     common_chat_params data;
 
-    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    // V4 uses the same DSML markup as V3.2, but names the tool call block "tool_calls"
+    // instead of "function_calls", renders tool results in tool call order and its
+    // non-thinking generation prompt ends with a bare </think> instead of an empty
+    // <think></think> pair.
+    const bool is_v4 = tmpl.source().find("function_calls") == std::string::npos;
+
+    std::optional<json> adjusted_messages;
+    if (is_v4) {
+        adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages);
+    }
+
+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages);
+    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages);
     data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking  = true;
     data.thinking_start_tag = "<think>";
@@ -1879,8 +1958,9 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
     const std::string DSML         = "｜DSML｜";
     const std::string THINK_START  = "<think>";
     const std::string THINK_END    = "</think>";
-    const std::string FC_START     = "<" + DSML + "function_calls>";
-    const std::string FC_END       = "</" + DSML + "function_calls>";
+    const std::string TC_BLOCK     = is_v4 ? "tool_calls" : "function_calls";
+    const std::string FC_START     = "<" + DSML + TC_BLOCK + ">";
+    const std::string FC_END       = "</" + DSML + TC_BLOCK + ">";
     const std::string INVOKE_START = "<" + DSML + "invoke";
     const std::string INVOKE_END   = "</" + DSML + "invoke>";
     const std::string PARAM_START  = "<" + DSML + "parameter";
@@ -1907,8 +1987,11 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
             reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
         } else if (extract_reasoning) {
             // Thinking disabled but reasoning extraction requested: the generation prompt
-            // contains an empty <think></think> pair that must still be consumed.
-            reasoning = p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
+            // contains an empty <think></think> pair (V3.2) or a bare </think> (V4) that
+            // must still be consumed.
+            reasoning = is_v4
+                ? p.optional(p.literal(THINK_END))
+                : p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
         }
 
         if (has_response_format) {
@@ -2612,12 +2695,14 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_gigachat_v3(tmpl, params);
     }
 
-    // DeepSeek V3.2 format detection: template defines dsml_token and uses it for tool calls.
+    // DeepSeek V3.2/V4 format detection: template defines dsml_token and uses it for tool calls.
     // The template source contains the token as a variable assignment, not as a literal in markup.
+    // V3.2 names the tool call block "function_calls", V4 names it "tool_calls".
     if (src.find("dsml_token") != std::string::npos &&
-        src.find("function_calls") != std::string::npos &&
-        src.find("DSML") != std::string::npos) {
-        LOG_DBG("Using specialized template: DeepSeek V3.2\n");
+        src.find("DSML") != std::string::npos &&
+        (src.find("function_calls") != std::string::npos ||
+         src.find("tool_calls") != std::string::npos)) {
+        LOG_DBG("Using specialized template: DeepSeek V3.2/V4\n");
         return common_chat_params_init_deepseek_v3_2(tmpl, params);
     }
 

common/jinja/caps.cpp

@@ -23,6 +23,7 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
     ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
     ctx.set_val("clear_thinking",            mk_val<value_bool>(!enabled));
     ctx.set_val("truncate_history_thinking", mk_val<value_bool>(!enabled));
+    ctx.set_val("drop_thinking",            mk_val<value_bool>(!enabled));
 }
 
 static void caps_try_execute(jinja::program & prog,

conversion/bert.py

@@ -369,12 +369,13 @@ class NomicBertModel(BertModel):
         return super().filter_tensors(item)
 
     def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
-        n_experts = self.find_hparam(["num_local_experts", "num_experts"])
         if "mlp.experts.mlp.w1" in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
             data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
             name += ".weight"
 
         if "mlp.experts.mlp.w2" in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
             data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
             data_torch = data_torch.transpose(1, 2)
             name += ".weight"

ggml/src/CMakeLists.txt

@@ -430,7 +430,7 @@ if (GGML_CPU_ALL_VARIANTS)
             message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}")
         endif()
     elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC")
-        if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+        if (CMAKE_SYSTEM_NAME MATCHES "Linux|AIX")
             ggml_add_cpu_backend_variant(power0)
             ggml_add_cpu_backend_variant(power7_1       POWER7)
             ggml_add_cpu_backend_variant(power7_2       POWER7  VSX)

ggml/src/ggml-backend.cpp

@@ -1546,11 +1546,23 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
     std::vector<int32_t> ids;
     std::vector<ggml_bitset_t> used_ids;
 
+    int prev_backend_id = -1;
+
     for (int split_id = 0; split_id < sched->n_splits; split_id++) {
         struct ggml_backend_sched_split * split = &splits[split_id];
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
+        // ensure the previous split's async work has completed before we start
+        // this split, the allocator may have reused buffer regions across splits
+        if (prev_backend_id >= 0 && prev_backend_id != split_backend_id) {
+            if (sched->events[prev_backend_id][sched->cur_copy] != NULL) {
+                ggml_backend_event_synchronize(sched->events[prev_backend_id][sched->cur_copy]);
+            } else {
+                ggml_backend_synchronize(sched->backends[prev_backend_id]);
+            }
+        }
+
         // copy the input tensors to the split backend
         for (int input_id = 0; input_id < split->n_inputs; input_id++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
@@ -1713,12 +1725,12 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
             }
         }
 
-        // record the event of this copy
-        if (split->n_inputs > 0) {
-            if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy], split_backend);
-            }
+        // record the event of this split
+        if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+            ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy], split_backend);
         }
+
+        prev_backend_id = split_backend_id;
     }
 
     return GGML_STATUS_SUCCESS;

ggml/src/ggml-cpu/llamafile/sgemm.cpp

@@ -2329,7 +2329,7 @@ class tinyBLAS_Q0_PPC {
         mc = 32;
         nc = 32;
         kc = 32;
-        n_chunk = 32
+        n_chunk = 32;
     #endif
         int64_t n_aligned = 0;
         if (n % n_chunk == 0) {

ggml/src/ggml-cuda/common.cuh

@@ -362,6 +362,15 @@ static bool blackwell_mma_available(const int cc) {
            ggml_cuda_highest_compiled_arch(cc) < GGML_CUDA_CC_RUBIN;
 }
 
+// Checks whether the tensor's base data pointer and higher-dimensional strides are byte-aligned to `alignment` bytes.
+static bool ggml_cuda_is_aligned(const ggml_tensor * tensor, const size_t alignment) {
+    GGML_ASSERT(tensor != nullptr);
+    return (reinterpret_cast<uintptr_t>(tensor->data) % alignment) == 0 &&
+           tensor->nb[1] % alignment == 0 &&
+           tensor->nb[2] % alignment == 0 &&
+           tensor->nb[3] % alignment == 0;
+}
+
 static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
 #if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
     return 64;

ggml/src/ggml-cuda/mmq.cu

@@ -130,14 +130,20 @@ void ggml_cuda_mul_mat_q(
         const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * y_block_size/y_values_per_block +
             ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq);
         ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
+        ggml_cuda_pool_alloc<float> src1_scale(ctx.pool());
+        if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) {
+            src1_scale.alloc(ne13*ne12*ne11);
+        }
 
         {
             const int64_t s11 = src1->nb[1] / ts_src1;
             const int64_t s12 = src1->nb[2] / ts_src1;
             const int64_t s13 = src1->nb[3] / ts_src1;
             if (use_native_fp4) {
+                static constexpr size_t align_float8 = 32;
+                const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8);
                 static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1));
-                quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
+                quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13, ne10_padded,
                                         ne11, ne12, ne13, stream);
 
             } else {
@@ -155,6 +161,7 @@ void ggml_cuda_mul_mat_q(
 
         const mmq_args args = {
             src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d,
+            src0->type == GGML_TYPE_NVFP4 && use_native_fp4 ? src1_scale.ptr : nullptr,
             ne00, ne01, ne1, s01, ne11, s1,
             ne02, ne12, s02, s12, s2,
             ne03, ne13, s03, s13, s3,
@@ -192,6 +199,10 @@ void ggml_cuda_mul_mat_q(
     const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * y_block_size/y_values_per_block +
         ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq);
     ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
+    ggml_cuda_pool_alloc<float> src1_scale(ctx.pool());
+    if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) {
+        src1_scale.alloc(ne12*n_expert_used);
+    }
 
     const int64_t ne11_flat = ne12*n_expert_used;
     const int64_t ne12_flat = 1;
@@ -202,18 +213,19 @@ void ggml_cuda_mul_mat_q(
         const int64_t s12 = src1->nb[2] / ts_src1;
         const int64_t s13 = src1->nb[3] / ts_src1;
 
-        if (dedup_bcast) {
-            // quantize each token once, scatter its block to all n_expert_used slots
-            if (use_native_fp4) {
-                quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
+        if (use_native_fp4) {
+            static constexpr size_t align_float8 = 32;
+            const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8);
+            if (dedup_bcast) {
+                quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10,
                                         /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
             } else {
-                quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
-                                        /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
+                quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13,
+                                        ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
             }
-        } else if (use_native_fp4) {
-            quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
-                                    ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
+        } else if (dedup_bcast) {
+            quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
+                                    /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
         } else {
             quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
                                    ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
@@ -229,6 +241,7 @@ void ggml_cuda_mul_mat_q(
     // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid.
     const mmq_args args = {
         src0_d, src0->type, (const int *) src1_q8_1.get(), ids_dst.get(), expert_bounds.get(), dst_d,
+        src1_scale.ptr,
         ne00, ne01, ne_get_rows, s01, ne_get_rows, s1,
         ne02, ne02, s02, s12, s2,
         ne03, ne13, s03, s13, s3,

ggml/src/ggml-cuda/mmq.cuh

@@ -13,7 +13,7 @@
 typedef void (*ggml_cuda_mmq_load_tiles_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride);
 typedef void (*ggml_cuda_mmq_vec_dot_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00);
 typedef void (*ggml_cuda_mmq_write_back_t)(const float * __restrict__ sum, const int32_t * __restrict__ get_rows_to_sorted,
-    float * __restrict__ dst, const int stride, const int i_max, const int j_max);
+    float * __restrict__ dst, const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max);
 
 enum mmq_q8_1_ds_layout {
     MMQ_Q8_1_DS_LAYOUT_D4,
@@ -413,11 +413,13 @@ static __host__ int ggml_cuda_mmq_get_nbytes_shared_x(const ggml_cuda_mmq_config
 
 template <ggml_type type, int J, bool fallback> static __device__ __forceinline__ void ggml_cuda_mmq_write_back_dp4a(
         const float * __restrict__ sum, const int32_t * __restrict__ ids_dst, float * __restrict__ dst,
-        const int stride, const int i_max, const int j_max) {
+        const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) {
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
     constexpr int nwarps    = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size;
     constexpr int I         = ggml_cuda_mmq_get_I(type, J, fallback);
 
+    const bool y_scale_used = y_scale != nullptr;
+
 #pragma unroll
     for (int j0 = 0; j0 < J; j0 += nwarps) {
         const int j = j0 + threadIdx.y;
@@ -434,7 +436,16 @@ template <ggml_type type, int J, bool fallback> static __device__ __forceinline_
                 continue;
             }
 
-            dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                if (y_scale_used) {
+                    dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+                } else {
+                    dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+                }
+            } else {
+                dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+                GGML_UNUSED(y_scale_used);
+            }
         }
     }
 }
@@ -442,7 +453,8 @@ template <ggml_type type, int J, bool fallback> static __device__ __forceinline_
 template<ggml_type type, int J, bool fallback>
 static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma(
             const float * __restrict__ sum, const int * __restrict__ ids_dst, float * __restrict__ dst,
-            const int stride, const int i_max, const int j_max) {
+            const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) {
+
 #if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
     typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
 #else
@@ -457,6 +469,8 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma(
 
     const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I);
 
+    const bool y_scale_used = y_scale != nullptr;
+
 #pragma unroll
     for (int j0 = 0; j0 < J; j0 += ntx*tile_C::J) {
 #pragma unroll
@@ -475,7 +489,16 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma(
                     continue;
                 }
 
-                dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                if constexpr (type == GGML_TYPE_NVFP4) {
+                    if (y_scale_used) {
+                        dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                    } else {
+                        dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                    }
+                } else {
+                    dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                    GGML_UNUSED(y_scale_used);
+                }
             }
         }
     }
@@ -819,6 +842,7 @@ template <ggml_type type, int J, bool fallback, bool fixup>
 static __device__ __forceinline__ void mul_mat_q_process_tile(
         const char * __restrict__ x, const int offset_x, const int * __restrict__ y,
         const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+        const float * __restrict__ y_scale,
         const int stride_row_x, const int ncols_y, const int stride_col_dst,
         const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) {
 
@@ -884,9 +908,9 @@ static __device__ __forceinline__ void mul_mat_q_process_tile(
     }
 
     if (fixup) {
-        write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), I, I, J);
+        write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), y_scale, I, I, J);
     } else {
-        write_back(sum, ids_dst, dst, stride_col_dst, tile_x_max_i, tile_y_max_j);
+        write_back(sum, ids_dst, dst, y_scale, stride_col_dst, tile_x_max_i, tile_y_max_j);
     }
 }
 
@@ -898,6 +922,7 @@ __launch_bounds__(ggml_cuda_mmq_get_nthreads(type, J, fallback), ggml_cuda_mmq_g
 static __global__ void mul_mat_q(
         const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst,
         const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+        const float * __restrict__ y_scale,
         const uint3 blocks_per_ne00, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst,
         const uint3 channel_ratio, const uint3 nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
         const uint3 sample_ratio, const uint3 nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
@@ -943,8 +968,14 @@ static __global__ void mul_mat_q(
         int col_low    = 0;
         int col_high   = ncols_dst;
         int col_diff   = ncols_dst;
-        int offset_y   = wt*stride_sample_y   + zt*stride_channel_y;
-        int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y       = wt*stride_sample_y   + zt*stride_channel_y;
+        int offset_dst     = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y_scale;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y;
+        } else {
+            GGML_UNUSED(offset_y_scale);
+        }
 
         if (ids_dst) {
             col_low  = expert_bounds[zt + 0];
@@ -953,6 +984,9 @@ static __global__ void mul_mat_q(
 
             offset_y   = 0;
             offset_dst = 0;
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                offset_y_scale = 0;
+            }
 
             if (jt*J >= col_diff) {
                 return;
@@ -974,6 +1008,11 @@ static __global__ void mul_mat_q(
 
         offset_y   += (col_low + jt*J)*(sizeof(block_q8_1_mmq)/sizeof(int));
         offset_dst += it*I;
+        const float * y_scale_tile = nullptr;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale += col_low + jt*J;
+            y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr;
+        }
 
         const int tile_x_max_i = nrows_x  - it*I - 1;
         const int tile_y_max_j = col_diff - jt*J - 1;
@@ -982,7 +1021,8 @@ static __global__ void mul_mat_q(
 
         constexpr bool fixup = false;
         mul_mat_q_process_tile<type, J, fallback, fixup>
-            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
+            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile,
+             stride_row_x, ncols_y, stride_col_dst,
              tile_x_max_i, tile_y_max_j, 0, blocks_per_ne00.z);
         return;
     }
@@ -1016,8 +1056,14 @@ static __global__ void mul_mat_q(
         int col_low    = 0;
         int col_high   = ncols_dst;
         int col_diff   = ncols_dst;
-        int offset_y   = wt*stride_sample_y   + zt*stride_channel_y;
-        int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y       = wt*stride_sample_y   + zt*stride_channel_y;
+        int offset_dst     = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y_scale;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y;
+        } else {
+            GGML_UNUSED(offset_y_scale);
+        }
 
         if (ids_dst) {
             col_low  = expert_bounds[zt + 0];
@@ -1026,6 +1072,9 @@ static __global__ void mul_mat_q(
 
             offset_y   = 0;
             offset_dst = 0;
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                offset_y_scale = 0;
+            }
 
             if (jt*J >= col_diff) {
                 kbc += blocks_per_ne00.z;
@@ -1053,6 +1102,11 @@ static __global__ void mul_mat_q(
 
         offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int));
         offset_dst += it*I;
+        const float * y_scale_tile = nullptr;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale += col_low + jt * J;
+            y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr;
+        }
 
         const int tile_x_max_i = nrows_x  - it*I - 1;
         const int tile_y_max_j = col_diff - jt*J - 1;
@@ -1061,7 +1115,8 @@ static __global__ void mul_mat_q(
 
         constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
         mul_mat_q_process_tile<type, J, fallback, fixup>
-            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
+            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile,
+             stride_row_x, ncols_y, stride_col_dst,
              tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop);
 
         kbc += blocks_per_ne00.z;
@@ -1090,8 +1145,14 @@ static __global__ void mul_mat_q(
     int col_low    = 0;
     int col_high   = ncols_dst;
     int col_diff   = ncols_dst;
-    int offset_y   = wt*stride_sample_y   + zt*stride_channel_y;
-    int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+    int offset_y       = wt*stride_sample_y   + zt*stride_channel_y;
+    int offset_dst     = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+    int offset_y_scale;
+    if constexpr (type == GGML_TYPE_NVFP4) {
+        offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y;
+    } else {
+        GGML_UNUSED(offset_y_scale);
+    }
 
     if (ids_dst) {
         col_low  = expert_bounds[zt + 0];
@@ -1100,6 +1161,9 @@ static __global__ void mul_mat_q(
 
         offset_y   = 0;
         offset_dst = 0;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale = 0;
+        }
 
         if (jt*J >= col_diff) {
             return;
@@ -1122,6 +1186,11 @@ static __global__ void mul_mat_q(
 
     offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int));
     offset_dst += it*I;
+    const float * y_scale_tile = nullptr;
+    if constexpr (type == GGML_TYPE_NVFP4) {
+        offset_y_scale += col_low + jt * J;
+        y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr;
+    }
 
     const int tile_x_max_i = nrows_x  - it*I - 1;
     const int tile_y_max_j = col_diff - jt*J - 1;
@@ -1130,7 +1199,8 @@ static __global__ void mul_mat_q(
 
     constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
     mul_mat_q_process_tile<type, J, fallback, fixup>
-        (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
+        (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile,
+         stride_row_x, ncols_y, stride_col_dst,
          tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop);
 }
 
@@ -1274,6 +1344,7 @@ static __global__ void mul_mat_q_stream_k_fixup(
 
 struct mmq_args {
     const char * x; ggml_type type_x; const int * y; const int32_t * ids_dst; const int32_t * expert_bounds; float * dst;
+    const float * y_scale;
     int64_t ncols_x; int64_t nrows_x; int64_t ncols_dst; int64_t stride_row_x; int64_t ncols_y; int64_t nrows_dst;
     int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst;
     int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst;
@@ -1323,7 +1394,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
 
     if (!ggml_cuda_mmq_get_stream_k(type, J, fallback, cc)) {
         mul_mat_q<type, J, fallback><<<block_nums_xy_tiling, block_dims, nbytes_shared, stream>>>
-            (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr,
+            (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, args.y_scale,
              blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
              channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
              sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
@@ -1352,7 +1423,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
     const dim3 block_dims_fixup(block_dims.x, block_dims.y/2, block_dims.z);
 
     mul_mat_q<type, J, fallback><<<block_nums_stream_k, block_dims, nbytes_shared, stream>>>
-        (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr,
+        (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.y_scale,
          blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
          channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
          sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,

ggml/src/ggml-cuda/quantize.cu

@@ -1,6 +1,55 @@
 #include "quantize.cuh"
 #include <cstdint>
 
+#if defined(BLACKWELL_MMA_AVAILABLE)
+// this maps to 256-bit loads in PTX on supported devices,
+// and otherwise falls back to 2 128-bit loads
+struct __builtin_align__(32) float8 {
+    float x; float y; float z; float w;
+    float p; float q; float r; float s;
+};
+#endif
+
+#if CUDART_VERSION >= 12080
+static __device__ __forceinline__ float nvfp4_native_scale_error(
+        const float vals[QK_NVFP4_SUB], const float inv_col_scale, const float inv_scale, const float scale) {
+    const float scale_dequant = 2.0f * scale;
+    float err = 0.0f;
+
+#pragma unroll
+    for (int k = 0; k < QK_NVFP4_SUB; k += 4) {
+        const float v0 = vals[k + 0] * inv_col_scale;
+        const float v1 = vals[k + 1] * inv_col_scale;
+        const float v2 = vals[k + 2] * inv_col_scale;
+        const float v3 = vals[k + 3] * inv_col_scale;
+
+        const __nv_fp4x4_e2m1 q(make_float4(v0 * inv_scale, v1 * inv_scale, v2 * inv_scale, v3 * inv_scale));
+        const __nv_fp4x4_storage_t q_storage = q.__x;
+        const __nv_fp4x2_storage_t q_lo = static_cast<__nv_fp4x2_storage_t>(q_storage);
+        const __nv_fp4x2_storage_t q_hi = static_cast<__nv_fp4x2_storage_t>(q_storage >> 8U);
+
+        const __half2_raw hraw2_lo = __nv_cvt_fp4x2_to_halfraw2(q_lo, __NV_E2M1);
+        const __half2_raw hraw2_hi = __nv_cvt_fp4x2_to_halfraw2(q_hi, __NV_E2M1);
+        const __half2 h2_lo = static_cast<__half2>(hraw2_lo);
+        const __half2 h2_hi = static_cast<__half2>(hraw2_hi);
+        const float2 dq_lo = __half22float2(h2_lo);
+        const float2 dq_hi = __half22float2(h2_hi);
+
+        const float err0 = fabsf(v0) - fabsf(dq_lo.x) * scale_dequant;
+        const float err1 = fabsf(v1) - fabsf(dq_lo.y) * scale_dequant;
+        const float err2 = fabsf(v2) - fabsf(dq_hi.x) * scale_dequant;
+        const float err3 = fabsf(v3) - fabsf(dq_hi.y) * scale_dequant;
+
+        err = fmaf(err0, err0, err);
+        err = fmaf(err1, err1, err);
+        err = fmaf(err2, err2, err);
+        err = fmaf(err3, err3, err);
+    }
+
+    return err;
+}
+#endif // CUDART_VERSION >= 12080
+
 __launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1)
 static __global__ void quantize_q8_1(
         const float * x_ptr, void * vy_ptr,
@@ -74,115 +123,209 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
     return static_cast<uint8_t>(biased);
 }
 
-
 // scatter: grid over tokens, quantize once, write to all the token's compact rows
-template <bool scatter>
+template <bool scatter, bool use_aligned_float8>
 static __global__ void quantize_mmq_nvfp4(
-        const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy,
+        const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, float * __restrict__ scale,
         const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
         const int64_t ne0, const int64_t ne1, const int64_t ne2, const int n_expert_used) {
 #if defined(BLACKWELL_MMA_AVAILABLE)
 
-    const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB;
-    if (i0_base >= ne0) {
-        return;
-    }
-
-    const int64_t k_block = i0_base / QK_FP4_MMQ;
     const int64_t blocks_per_col = (ne0 + QK_FP4_MMQ - 1) / QK_FP4_MMQ;
-    if (k_block >= blocks_per_col) {
-        return;
-    }
-    const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB;
 
     int64_t base_idx;
     if constexpr (scatter) {
         base_idx = (int64_t) blockIdx.x * s02; // one physical row per token
     } else {
-        const int64_t i2  = blockIdx.z % ne2;
-        const int64_t i3  = blockIdx.z / ne2;
+        const int64_t i2  = blockIdx.y % ne2;
+        const int64_t i3  = blockIdx.y / ne2;
         const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x;
         base_idx = i3 * s03 + i2 * s02 + i01 * s01;
     }
+    const float * __restrict__  x_row = x + base_idx;
 
-    float vals_raw[QK_NVFP4_SUB];
-    float amax_raw = 0.0f;
+    float amax = 0.0f;
+    if constexpr (use_aligned_float8) {
+        for (int64_t i0 = 8 * threadIdx.x; i0 < ne00; i0 += 8 * blockDim.x) {
+            const float * x_base = x_row + i0;
+            const float8 v = reinterpret_cast<const float8 *>(x_base)[0];
+            amax = fmaxf(amax, fabsf(v.x));
+            amax = fmaxf(amax, fabsf(v.y));
+            amax = fmaxf(amax, fabsf(v.z));
+            amax = fmaxf(amax, fabsf(v.w));
+            amax = fmaxf(amax, fabsf(v.p));
+            amax = fmaxf(amax, fabsf(v.q));
+            amax = fmaxf(amax, fabsf(v.r));
+            amax = fmaxf(amax, fabsf(v.s));
+        }
+    } else {
+        for (int64_t i0 = threadIdx.x; i0 < ne00; i0 += blockDim.x) {
+            amax = fmaxf(amax, fabsf(x_row[i0]));
+        }
+    }
+
+    amax = warp_reduce_max<WARP_SIZE>(amax);
+
+    __shared__ float warp_amax[CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE];
+    const int lane = threadIdx.x % WARP_SIZE;
+    const int warp = threadIdx.x / WARP_SIZE;
+
+    if (lane == 0) {
+        warp_amax[warp] = amax;
+    }
+    __syncthreads();
+
+    if (warp == 0) {
+        amax = threadIdx.x < int(CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE) ? warp_amax[lane] : 0.0f;
+        amax = warp_reduce_max<WARP_SIZE>(amax);
+        if (lane == 0) {
+            warp_amax[0] = amax / (6.0f * 448.0f);
+            if constexpr (scatter) {
 #pragma unroll
-    for (int k = 0; k < QK_NVFP4_SUB; k++) {
-        const int64_t i00 = i0_base + k;
-        if (i00 < ne00) {
-            const float v = x[base_idx + i00];
-            vals_raw[k] = v;
-            amax_raw = fmaxf(amax_raw, fabsf(v));
-        } else {
-            vals_raw[k] = 0.0f;
+                for (int slot = 0; slot < n_expert_used; ++slot) {
+                    const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
+                    scale[i] = warp_amax[0];
+                }
+            } else {
+                scale[blockIdx.y * ne1 + blockIdx.x] = warp_amax[0];
+            }
         }
     }
-
-    static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2};
-    const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f);
-
-    float best_err = FLT_MAX;
-    uint8_t fp8_code = 0;
-    float subblock_scale = 0.0f;
-
-#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell.
-    for (int i = 0; i < 5; i++) {
-        const int test_code = first_fp8_code + test_offsets[i];
-        if (test_code < 0 || test_code > 0x7e) {
-            continue;
-        }
-        const uint8_t code = (uint8_t) test_code;
-        const float test_scale = ggml_cuda_ue4m3_to_fp32(code);
-        const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
-        float cur_err = 0.0f;
-#pragma unroll
-        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
-            const float v = vals_raw[k];
-            const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
-            const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale;
-            cur_err = fmaf(err_diff, err_diff, cur_err);
-        }
-
-        if (cur_err < best_err) {
-            best_err = cur_err;
-            fp8_code = test_code;
-            subblock_scale = test_scale;
-        }
-    }
-
-    const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
-    uint32_t q0 = 0;
-    uint32_t q1 = 0;
-#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1
-    for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
-        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  0], inv_scale) << (8 * k);
-        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  8], inv_scale) << (8 * k + 4);
-        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  4], inv_scale) << (8 * k);
-        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4);
-    }
+    __syncthreads();
 
     block_fp4_mmq * y = (block_fp4_mmq *) vy;
-    if constexpr (scatter) {
+    const int64_t n_subblocks = (ne0 + QK_NVFP4_SUB - 1) / QK_NVFP4_SUB;
+
+    for (int64_t isb = threadIdx.x; isb < n_subblocks; isb += blockDim.x) {
+        const int64_t i0_base = isb * QK_NVFP4_SUB;
+        const int64_t k_block = i0_base / QK_FP4_MMQ;
+        const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB;
+
+        const float row_scale = warp_amax[0];
+        const float inv_col_scale = row_scale > 0.0f ? 1.0f / row_scale : 0.0f;
+
+        float vals[QK_NVFP4_SUB];
+        if constexpr (use_aligned_float8) {
+            const float * x_base = x_row + i0_base;
+            const float8 v0 = i0_base +  7 < ne00 ? reinterpret_cast<const float8 *>(x_base)[0]     : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
+            const float8 v1 = i0_base + 15 < ne00 ? reinterpret_cast<const float8 *>(x_base + 8)[0] : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
+            vals[0] = v0.x; vals[1] = v0.y; vals[2] = v0.z; vals[3] = v0.w;
+            vals[4] = v0.p; vals[5] = v0.q; vals[6] = v0.r; vals[7] = v0.s;
+            vals[8] = v1.x; vals[9] = v1.y; vals[10] = v1.z; vals[11] = v1.w;
+            vals[12] = v1.p; vals[13] = v1.q; vals[14] = v1.r; vals[15] = v1.s;
+        } else {
 #pragma unroll
-        for (int slot = 0; slot < n_expert_used; ++slot) {
-            const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
-            block_fp4_mmq * yb = y + (k_block * ne1 + i);
+            for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+                const int64_t i00 = i0_base + k;
+                vals[k] = i00 < ne00 ? x_row[i00] : 0.0f;
+            }
+        }
+
+        uint32_t q0 = 0;
+        uint32_t q1 = 0;
+
+        float amax_sub = 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+            amax_sub = fmaxf(amax_sub, fabsf(vals[k] * inv_col_scale));
+        }
+
+        static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2 };
+        const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_sub / 6.0f);
+
+        uint8_t fp8_code = (uint8_t) first_fp8_code;
+        float subblock_scale = ggml_cuda_ue4m3_to_fp32(fp8_code);
+        float inv_scale_err = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+#if CUDART_VERSION >= 12080
+        float best_err = nvfp4_native_scale_error(vals, inv_col_scale, inv_scale_err, subblock_scale);
+#else
+        float best_err = 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+            const float v = vals[k] * inv_col_scale;
+            const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, inv_scale_err);
+            const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * subblock_scale;
+            best_err = fmaf(err_diff, err_diff, best_err);
+        }
+#endif // CUDART_VERSION >= 12080
+
+#pragma unroll
+        for (int i = 1; i < 5; ++i) {
+            const int test_code = first_fp8_code + test_offsets[i];
+            if (test_code < 0 || test_code > 0x7e) {
+                continue;
+            }
+
+            const float test_scale = ggml_cuda_ue4m3_to_fp32((uint8_t) test_code);
+            const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
+#if CUDART_VERSION >= 12080
+            const float cur_err = nvfp4_native_scale_error(vals, inv_col_scale, test_inv_scale, test_scale);
+#else
+            float cur_err = 0.0f;
+#pragma unroll
+            for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+                const float v = vals[k] * inv_col_scale;
+                const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
+                const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * test_scale;
+                cur_err = fmaf(err_diff, err_diff, cur_err);
+            }
+#endif // CUDART_VERSION >= 12080
+
+            if (cur_err < best_err) {
+                best_err = cur_err;
+                fp8_code = (uint8_t) test_code;
+                subblock_scale = test_scale;
+            }
+        }
+#if CUDART_VERSION >= 12080
+        const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+        const float s = inv_col_scale * inv_scale;
+
+        __nv_fp4x4_e2m1 q0_lo(make_float4(vals[0] * s, vals[8]  * s, vals[1] * s, vals[9]  * s));
+        __nv_fp4x4_e2m1 q0_hi(make_float4(vals[2] * s, vals[10] * s, vals[3] * s, vals[11] * s));
+        __nv_fp4x4_e2m1 q1_lo(make_float4(vals[4] * s, vals[12] * s, vals[5] * s, vals[13] * s));
+        __nv_fp4x4_e2m1 q1_hi(make_float4(vals[6] * s, vals[14] * s, vals[7] * s, vals[15] * s));
+
+        const char2 q0_lo_c = *reinterpret_cast<char2 *>(&q0_lo);
+        const char2 q0_hi_c = *reinterpret_cast<char2 *>(&q0_hi);
+        const char2 q1_lo_c = *reinterpret_cast<char2 *>(&q1_lo);
+        const char2 q1_hi_c = *reinterpret_cast<char2 *>(&q1_hi);
+
+        q0 = uint32_t(uint8_t(q0_lo_c.x)) | (uint32_t(uint8_t(q0_lo_c.y)) <<  8) |
+            (uint32_t(uint8_t(q0_hi_c.x)) << 16) | (uint32_t(uint8_t(q0_hi_c.y)) << 24);
+        q1 = uint32_t(uint8_t(q1_lo_c.x)) | (uint32_t(uint8_t(q1_lo_c.y)) <<  8) |
+            (uint32_t(uint8_t(q1_hi_c.x)) << 16) | (uint32_t(uint8_t(q1_hi_c.y)) << 24);
+#else
+        const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
+            q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 0] * inv_col_scale, inv_scale)) << (8 * k);
+            q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 8] * inv_col_scale, inv_scale)) << (8 * k + 4);
+            q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 4] * inv_col_scale, inv_scale)) << (8 * k);
+            q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 12] * inv_col_scale, inv_scale)) << (8 * k + 4);
+        }
+#endif // CUDART_VERSION >= 12080
+
+        if constexpr (scatter) {
+#pragma unroll
+            for (int slot = 0; slot < n_expert_used; ++slot) {
+                const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
+                block_fp4_mmq * yb = y + (k_block * ne1 + i);
+                uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
+                yqs[2 * sub + 0] = q0;
+                yqs[2 * sub + 1] = q1;
+                reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
+            }
+        } else {
+            block_fp4_mmq * yb = y + (blockIdx.y * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x);
             uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
             yqs[2 * sub + 0] = q0;
             yqs[2 * sub + 1] = q1;
             reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
         }
-    } else {
-        block_fp4_mmq * yb = y + (blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x);
-        uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
-        yqs[2 * sub + 0] = q0;
-        yqs[2 * sub + 1] = q1;
-        reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
     }
-    GGML_UNUSED(n_expert_used);
 #else
-    GGML_UNUSED(n_expert_used);
+    GGML_UNUSED_VARS(x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, n_expert_used);
     NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only.
 #endif // defined(BLACKWELL_MMA_AVAILABLE)
 
@@ -491,18 +634,22 @@ void quantize_scatter_mmq_q8_1_cuda(
 
 // scatter=true reuses the quant kernels: grid over tokens, ids = inverse map (token slot -> compact row)
 void quantize_scatter_mmq_fp4_cuda(
-        const float * x, const int32_t * ids_src1_inv, void * vy, const ggml_type type_src0,
+        const float * x, const int32_t * ids_src1_inv, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8,
         const int64_t ne00, const int64_t stride_token, const int64_t ne0,
         const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) {
     GGML_ASSERT(ne0 > 0);
     if (type_src0 == GGML_TYPE_NVFP4) {
+        GGML_ASSERT(scale);
         GGML_ASSERT(ne00 % QK_NVFP4 == 0);
-        constexpr int nvfp4_block_size = 128;
-        const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
-        const dim3 block_size(nvfp4_block_size, 1, 1);
-        const dim3 num_blocks(n_tokens, block_num_y, 1);
-        quantize_mmq_nvfp4<true><<<num_blocks, block_size, 0, stream>>>(
-            x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
+        const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
+        const dim3 num_blocks(n_tokens, 1, 1);
+        if (use_aligned_float8) {
+            quantize_mmq_nvfp4<true, true><<<num_blocks, block_size, 0, stream>>>(
+                x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
+        } else {
+            quantize_mmq_nvfp4<true, false><<<num_blocks, block_size, 0, stream>>>(
+                x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
+        }
     } else {
         GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4);
         constexpr int nwarps = 8;
@@ -516,20 +663,24 @@ void quantize_scatter_mmq_fp4_cuda(
 }
 
 void quantize_mmq_fp4_cuda(
-        const float * x, const int32_t * ids, void * vy, const ggml_type type_src0,
+        const float * x, const int32_t * ids, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8,
         const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
         const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
     GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4);
     GGML_ASSERT(ne0 > 0);
 
     if (type_src0 == GGML_TYPE_NVFP4) {
+        GGML_ASSERT(scale);
         GGML_ASSERT(ne00 % QK_NVFP4 == 0);
-        constexpr int nvfp4_block_size = 128;
-        const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
-        const dim3 block_size(nvfp4_block_size, 1, 1);
-        const dim3 num_blocks(ne1, block_num_y, ne2 * ne3);
-        quantize_mmq_nvfp4<false><<<num_blocks, block_size, 0, stream>>>(
-            x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
+        const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
+        const dim3 num_blocks(ne1, ne2 * ne3, 1);
+        if (use_aligned_float8) {
+            quantize_mmq_nvfp4<false, true><<<num_blocks, block_size, 0, stream>>>(
+                x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
+        } else {
+            quantize_mmq_nvfp4<false, false><<<num_blocks, block_size, 0, stream>>>(
+                x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
+        }
     } else {
         GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);
 

ggml/src/ggml-cuda/quantize.cuh

@@ -29,7 +29,9 @@ void quantize_mmq_q8_1_cuda(
 void quantize_mmq_fp4_cuda(const float *   x,
                              const int32_t * ids,
                              void *          vy,
+                             float *         scale,
                              ggml_type       type_src0,
+                             bool            use_aligned_float8,
                              int64_t         ne00,
                              int64_t         s01,
                              int64_t         s02,
@@ -44,7 +46,9 @@ void quantize_mmq_fp4_cuda(const float *   x,
 void quantize_scatter_mmq_fp4_cuda(const float *   x,
                                    const int32_t * ids_src1_inv,
                                    void *          vy,
+                                   float *         scale,
                                    ggml_type       type_src0,
+                                   bool            use_aligned_float8,
                                    int64_t         ne00,
                                    int64_t         stride_token,
                                    int64_t         ne0,

ggml/src/ggml-hexagon/ggml-hexagon.cpp

@@ -3084,7 +3084,10 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session
         return false;
     }
 
-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+    if (!ggml_is_contiguous_1(src0)) {
+        return false;
+    }
+    if (!ggml_is_contiguous(dst)) {
         return false;
     }
 
@@ -3095,7 +3098,7 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session
         if (!ggml_are_same_shape(src0, src1)) {
             return false;
         }
-        if (!ggml_is_contiguous(src1)) {
+        if (!ggml_is_contiguous_1(src1)) {
             return false;
         }
     }
@@ -4152,12 +4155,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
                 case GGML_UNARY_OP_SIGMOID:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_TANH:
-                    supp = ggml_hexagon_supported_unary(sess, op);
-                    break;
                 case GGML_UNARY_OP_SILU:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_QUICK:
-                    supp = ggml_hexagon_supported_activations(sess, op);
+                    supp = ggml_hexagon_supported_unary(sess, op);
                     break;
                 default:
                     break;

ggml/src/ggml-hexagon/htp/main.c

@@ -723,14 +723,14 @@ static int execute_op(struct htp_ops_context * octx) {
         case HTP_OP_SQRT:
         case HTP_OP_UNARY_SOFTPLUS:
         case HTP_OP_UNARY_SIGMOID:
+        case HTP_OP_UNARY_SILU:
+        case HTP_OP_UNARY_GELU:
         case HTP_OP_UNARY_NEG:
         case HTP_OP_UNARY_EXP:
         case HTP_OP_UNARY_TANH:
         case HTP_OP_L2_NORM:
             return op_unary(octx);
 
-        case HTP_OP_UNARY_SILU:
-        case HTP_OP_UNARY_GELU:
         case HTP_OP_GLU_SWIGLU:
         case HTP_OP_GLU_SWIGLU_OAI:
         case HTP_OP_GLU_GEGLU:

ggml/src/ggml-hexagon/htp/unary-ops.c

@@ -276,6 +276,39 @@ static void sigmoid_f32(const float * restrict src,
     }
 }
 
+// silu(x) = x * sigmoid(x)
+static void silu_f32(const float * restrict src,
+                     float * restrict dst,
+                     const uint32_t num_rows,
+                     const struct htp_unary_context * uctx) {
+    htp_unary_op_preamble;
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * dst_row_size_aligned);
+
+        hvx_sigmoid_f32_aa(dst_local, src_local, ne0);
+        hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0);
+    }
+}
+
+// gelu(x) = x * sigmoid(1.702 * x)  (quick/sigmoid approximation, matches CPU GELU_QUICK reference)
+static void gelu_f32(const float * restrict src,
+                     float * restrict dst,
+                     const uint32_t num_rows,
+                     const struct htp_unary_context * uctx) {
+    htp_unary_op_preamble;
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * dst_row_size_aligned);
+
+        hvx_mul_scalar_f32(dst_local, src_local, 1.702f, ne0);
+        hvx_sigmoid_f32_aa(dst_local, dst_local, ne0);
+        hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0);
+    }
+}
+
 static void tri_f32(const float * restrict src,
                     float * restrict dst,
                     const uint32_t num_rows,
@@ -566,6 +599,8 @@ DEFINE_UNARY_TASK(sqrt,           false, false, sqrt_f32(src0_vtcm, dst_vtcm, bl
 DEFINE_UNARY_TASK(unary_neg,      false, false, neg_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_exp,      false, false, exp_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_sigmoid,  false, false, sigmoid_f32(src0_vtcm, dst_vtcm, block_size, uctx))
+DEFINE_UNARY_TASK(unary_silu,     false, false, silu_f32(src0_vtcm, dst_vtcm, block_size, uctx))
+DEFINE_UNARY_TASK(unary_gelu,     false, false, gelu_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_softplus, false, false, softplus_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_tanh,     false, false, tanh_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(l2_norm,        false, false, l2_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx))
@@ -717,6 +752,19 @@ static inline void tile_unary_softplus_f32(uint8_t * dst_vtcm, const uint8_t * s
     }
 }
 
+// silu(x) = x * sigmoid(x)
+static inline void tile_silu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) {
+    hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw);
+    hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw);
+}
+
+// gelu(x) = x * sigmoid(1.702 * x)  (quick/sigmoid approximation, matches CPU GELU_QUICK reference)
+static inline void tile_gelu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) {
+    hvx_mul_scalar_f32(dst_vtcm, src_vtcm, 1.702f, tw);
+    hvx_sigmoid_f32_aa(dst_vtcm, dst_vtcm, tw);
+    hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw);
+}
+
 // Triangular mask applied to one column tile. Boundary is an absolute column index, so
 // each vector compares against its absolute column position (col_start + i*VLEN_FP32).
 static inline void tri_apply_tile_f32(const uint8_t * restrict src, uint8_t * restrict dst,
@@ -798,6 +846,8 @@ DEFINE_UNARY_TILED_TASK(sqrt,           false, hvx_sqrt_f32_aa(dst_vtcm, src_vtc
 DEFINE_UNARY_TILED_TASK(unary_neg,      false, hvx_scale_f32_aa(dst_vtcm, src_vtcm, tw, -1.0f))
 DEFINE_UNARY_TILED_TASK(unary_exp,      false, hvx_exp_f32(dst_vtcm, src_vtcm, tw, false))
 DEFINE_UNARY_TILED_TASK(unary_sigmoid,  false, hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw))
+DEFINE_UNARY_TILED_TASK(unary_silu,     false, tile_silu_f32(dst_vtcm, src_vtcm, tw))
+DEFINE_UNARY_TILED_TASK(unary_gelu,     false, tile_gelu_f32(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(unary_softplus, false, tile_unary_softplus_f32(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(unary_tanh,     false, hvx_tanh_f32_aa(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(tri,            true,  tri_apply_tile_f32(src_vtcm, dst_vtcm, tw, col, i01, ne0, tri_ttype))
@@ -821,6 +871,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
         case HTP_OP_UNARY_NEG:       op_type = "neg-f32";          break;
         case HTP_OP_UNARY_EXP:       op_type = "exp-f32";          break;
         case HTP_OP_UNARY_SIGMOID:   op_type = "sigmoid-f32";      break;
+        case HTP_OP_UNARY_SILU:      op_type = "silu-f32";         break;
+        case HTP_OP_UNARY_GELU:      op_type = "gelu-f32";         break;
         case HTP_OP_UNARY_SOFTPLUS:  op_type = "softplus-f32";     break;
         case HTP_OP_UNARY_TANH:      op_type = "tanh-f32";         break;
         case HTP_OP_L2_NORM:         op_type = "l2norm-f32";       break;
@@ -917,6 +969,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
                 case HTP_OP_UNARY_NEG:       task_func = unary_task_f32_tiled_unary_neg;      break;
                 case HTP_OP_UNARY_EXP:       task_func = unary_task_f32_tiled_unary_exp;      break;
                 case HTP_OP_UNARY_SIGMOID:   task_func = unary_task_f32_tiled_unary_sigmoid;  break;
+                case HTP_OP_UNARY_SILU:      task_func = unary_task_f32_tiled_unary_silu;     break;
+                case HTP_OP_UNARY_GELU:      task_func = unary_task_f32_tiled_unary_gelu;     break;
                 case HTP_OP_UNARY_SOFTPLUS:  task_func = unary_task_f32_tiled_unary_softplus; break;
                 case HTP_OP_UNARY_TANH:      task_func = unary_task_f32_tiled_unary_tanh;     break;
                 case HTP_OP_TRI:             task_func = unary_task_f32_tiled_tri;            break;
@@ -934,6 +988,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
                 case HTP_OP_UNARY_NEG:       task_func = unary_task_f32_unary_neg;            break;
                 case HTP_OP_UNARY_EXP:       task_func = unary_task_f32_unary_exp;            break;
                 case HTP_OP_UNARY_SIGMOID:   task_func = unary_task_f32_unary_sigmoid;        break;
+                case HTP_OP_UNARY_SILU:      task_func = unary_task_f32_unary_silu;           break;
+                case HTP_OP_UNARY_GELU:      task_func = unary_task_f32_unary_gelu;           break;
                 case HTP_OP_UNARY_SOFTPLUS:  task_func = unary_task_f32_unary_softplus;       break;
                 case HTP_OP_UNARY_TANH:      task_func = unary_task_f32_unary_tanh;           break;
                 case HTP_OP_L2_NORM:         task_func = unary_task_f32_l2_norm;              break;

ggml/src/ggml-hexagon/htp/unary-ops.h

@@ -51,6 +51,8 @@ static inline bool htp_op_is_unary(uint32_t opcode) {
         case HTP_OP_UNARY_NEG:
         case HTP_OP_UNARY_EXP:
         case HTP_OP_UNARY_SIGMOID:
+        case HTP_OP_UNARY_SILU:
+        case HTP_OP_UNARY_GELU:
         case HTP_OP_UNARY_SOFTPLUS:
         case HTP_OP_UNARY_TANH:
         case HTP_OP_L2_NORM:

ggml/src/ggml-metal/ggml-metal-device.m

@@ -1218,8 +1218,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                    (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_TIMESTEP_EMBEDDING:
-        case GGML_OP_LEAKY_RELU:
             return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_LEAKY_RELU:
+            return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
         case GGML_OP_ARGSORT:
         case GGML_OP_TOP_K:
         case GGML_OP_ARANGE:

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -2195,6 +2195,8 @@ struct ggml_backend_vk_context {
     // and set to true after the buffer contents are consumed.
     bool prealloc_x_need_sync, prealloc_y_need_sync, prealloc_split_k_need_sync;
 
+    bool compute_ctx_has_async_transfers {};
+
     vk_context_ref compute_ctx;
 
     vk_context_ref transfer_ctx;
@@ -3420,6 +3422,7 @@ static void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subct
 
     if (ctx) {
         ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false;
+        ctx->compute_ctx_has_async_transfers = false;
     }
 
     subctx->s->buffer->buf.pipelineBarrier(
@@ -3459,11 +3462,16 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events
         return;
     }
 
+    const bool transfer_queue = ctx->p->q->transfer_only;
+
     ctx->s->buffer->buf.waitEvents(
         events,
         ctx->p->q->stage_flags,
         ctx->p->q->stage_flags,
-        {},
+        { {
+          { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) },
+          { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }
+        } },
         {},
         {}
     );
@@ -7117,6 +7125,10 @@ static void ggml_vk_instance_init() {
         extensions.push_back("VK_EXT_debug_utils");
     }
     VkBool32 enable_best_practice = layer_settings;
+    VkBool32 enable_sync_validation = layer_settings && getenv("GGML_VK_SYNC_VALIDATE") != nullptr;
+    if (enable_sync_validation) {
+        std::cerr << "ggml_vulkan: Synchronization validation enabled" << std::endl;
+    }
     std::vector<vk::LayerSettingEXT> settings = {
         {
             "VK_LAYER_KHRONOS_validation",
@@ -7125,6 +7137,13 @@ static void ggml_vk_instance_init() {
             1,
             &enable_best_practice
         },
+        {
+            "VK_LAYER_KHRONOS_validation",
+            "validate_sync",
+            vk::LayerSettingTypeEXT::eBool32,
+            1,
+            &enable_sync_validation
+        },
     };
     vk::LayerSettingsCreateInfoEXT layer_setting_info(settings);
     vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions, &layer_setting_info);
@@ -15776,6 +15795,10 @@ static void ggml_backend_vk_set_tensor_2d_async(ggml_backend_t backend, ggml_ten
 
     bool ret = ggml_vk_buffer_write_2d_async(cpy_ctx, buf, dst_offset, data, stride_data, stride_tensor, size, n_copies);
 
+    if (ret && !ctx->device->async_use_transfer_queue) {
+        ctx->compute_ctx_has_async_transfers = true;
+    }
+
     if (!ret) {
         const size_t staging_size = size * n_copies;
         ggml_vk_ensure_sync_staging_buffer(ctx, staging_size);
@@ -15898,6 +15921,7 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_ba
         ggml_vk_buffer_copy_async(compute_ctx, dst_buf, vk_tensor_offset(dst) + dst->view_offs,
                                    src_buf_ctx->dev_buffer, vk_tensor_offset(src) + src->view_offs,
                                    ggml_nbytes(src));
+        ctx->compute_ctx_has_async_transfers = true;
         return true;
     }
 
@@ -15914,6 +15938,7 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_ba
             cpy_ctx = ggml_vk_get_transfer_ctx(ctx);
         } else {
             cpy_ctx = ggml_vk_get_compute_ctx(ctx);
+            ctx->compute_ctx_has_async_transfers = true;
         }
 
         return ggml_vk_buffer_write_async(cpy_ctx, dst_buf,
@@ -16554,6 +16579,20 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
     ggml_vk_submit_transfer_ctx(ctx);
 
+    if (ctx->compute_ctx_has_async_transfers) {
+        vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);
+        ggml_vk_sync_buffers(ctx, compute_ctx);
+    }
+
+    {
+        vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);
+        ggml_vk_sync_buffers(nullptr, compute_ctx);
+    }
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+    ggml_vk_synchronize(ctx);
+#endif
+
     vk_context compute_ctx;
     if (vk_perf_logger_enabled) {
         // allocate/resize the query pool
@@ -17290,7 +17329,11 @@ ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
     };
 
     if (!ctx->device->support_async) {
-        vk_backend->iface.get_tensor_async = nullptr;
+        vk_backend->iface.set_tensor_async    = nullptr;
+        vk_backend->iface.get_tensor_async    = nullptr;
+        vk_backend->iface.set_tensor_2d_async = nullptr;
+        vk_backend->iface.get_tensor_2d_async = nullptr;
+        vk_backend->iface.cpy_tensor_async    = nullptr;
     }
 
     return vk_backend;
@@ -17442,8 +17485,9 @@ static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml
     props->type        = ggml_backend_vk_device_get_type(dev);
     props->device_id   = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str();
     ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    const vk_device& device = ggml_vk_get_device(ctx->device);
     props->caps = {
-        /* .async                 = */ true,
+        /* .async                 = */ device->support_async,
         /* .host_buffer           = */ true,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ true,
@@ -19103,7 +19147,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph *
         ggml_vk_print_graph_origin(tensor, done);
     }
 
-    if (avg_err > 0.01 || std::isnan(avg_err)) {
+    if (avg_err > 0.1 || std::isnan(avg_err)) {
         std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
         std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
         if (src0 != nullptr) {

models/templates/deepseek-ai-DeepSeek-V4.jinja

@@ -8,12 +8,15 @@
     {%- set thinking = false -%}
   {%- endif -%}
 {%- endif -%}
+{%- if not drop_thinking is defined -%}
+  {%- set drop_thinking = false -%}
+{%- endif -%}
 {%- set dsml_token = '｜DSML｜' -%}
 {%- set thinking_start_token = '<think>' -%}
 {%- set thinking_end_token = '</think>' -%}
 {%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</' + dsml_token + 'parameter>\n...\n</' + dsml_token + 'invoke>\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n</' + dsml_token + 'invoke>\n</' + dsml_token + 'tool_calls>\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%}
 {%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%}
-{%- set ns = namespace(system_prompt='', is_first_sp=true) -%}
+{%- set ns = namespace(system_prompt='', is_first_sp=true, has_tool_calls=false) -%}
 {%- for message in messages -%}
   {%- if message['role'] == 'system' -%}
     {%- if ns.is_first_sp -%}
@@ -46,6 +49,11 @@
   {%- endif -%}
 {%- endfor -%}
 {%- set state = namespace(in_user=false) -%}
+{%- for message in messages -%}
+  {%- if message['role'] == 'tool' -%}
+    {%- set ns.has_tool_calls = true -%}
+  {%- endif -%}
+{%- endfor -%}
 {%- for message in messages -%}
   {%- if message['role'] == 'user' or message['role'] == 'developer' -%}
     {%- if state.in_user -%}
@@ -67,7 +75,8 @@
     {%- set state.in_user = false -%}
     {{- '<｜Assistant｜>' -}}
     {%- set is_after_last_user = loop.index0 > last_user_idx.value -%}
-    {%- if is_after_last_user and thinking -%}
+    {%- set retain_reasoning = (not drop_thinking) or (is_after_last_user or ns.has_tool_calls) -%}
+    {%- if retain_reasoning and thinking -%}
       {{- thinking_start_token -}}
       {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%}
         {{- message['reasoning_content'] -}}

tests/test-chat.cpp

@@ -109,6 +109,15 @@ static void assert_contains(const std::string & haystack, const std::string & ne
     }
 }
 
+static void assert_not_contains(const std::string & haystack, const std::string & needle) {
+    if (haystack.find(needle) != std::string::npos) {
+        LOG_ERR("Expected NOT to contain: %s\n", needle.c_str());
+        LOG_ERR("Actual: %s\n", haystack.c_str());
+        common_log_flush(common_log_main());
+        throw std::runtime_error("Test failed");
+    }
+}
+
 static void assert_ends_with(const std::string & str, const std::string & suffix) {
     if (str.size() < suffix.size() ||
         str.compare(str.size() - suffix.size(), suffix.size(), suffix) != 0) {
@@ -4016,6 +4025,132 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .run();
     }
 
+    // DeepSeek V4 tests - same DSML markup as V3.2, but the tool call block is named
+    // "tool_calls" and the non-thinking generation prompt ends in a bare </think>
+    // instead of an empty <think></think> pair.
+    {
+        auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V4.jinja", detailed_debug);
+
+        // Pure content (non-thinking mode; generation prompt ends with </think>)
+        tst.test("Hello, world!\nWhat's up?")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .expect(message_assist)
+            .run();
+
+        // Thinking + content
+        tst.test("I'm\nthinking</think>Hello, world!\nWhat's up?")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .expect(message_assist_thoughts)
+            .run();
+
+        // Thinking + tool call (single, string param)
+        tst.test(
+               "Let me check the time</think>\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"get_time\">\n"
+               "<｜DSML｜parameter name=\"city\" string=\"true\">Tokyo</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ get_time_tool })
+            .expect(message_with_tool_calls_and_reasoning("get_time", R"({"city": "Tokyo"})", "Let me check the time"))
+            .run();
+
+        // Tool call without reasoning (non-thinking mode), integer param (string="false")
+        tst.test(
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"special_function\">\n"
+               "<｜DSML｜parameter name=\"arg1\" string=\"false\">1</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ special_function_tool })
+            .expect(message_assist_call)
+            .run();
+
+        // Multiple parallel tool calls with reasoning
+        tst.test(
+               "Calling both</think>\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"get_time\">\n"
+               "<｜DSML｜parameter name=\"city\" string=\"true\">Paris</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "<｜DSML｜invoke name=\"get_weather\">\n"
+               "<｜DSML｜parameter name=\"city\" string=\"true\">Paris</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .parallel_tool_calls(true)
+            .tools({ get_time_tool, get_weather_tool })
+            .expect(message_with_reasoning_content_and_multiple_tool_calls(
+                "Calling both", "",
+                { { "get_time", R"({"city": "Paris"})" }, { "get_weather", R"({"city": "Paris"})" } }))
+            .run();
+
+        // Tool call with content before tool calls
+        tst.test(
+               "Thinking about it</think>"
+               "Let me call the function.\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"special_function\">\n"
+               "<｜DSML｜parameter name=\"arg1\" string=\"false\">1</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ special_function_tool })
+            .expect_reasoning("Thinking about it")
+            .expect_content("Let me call the function.")
+            .expect_tool_calls({
+                { "special_function", R"({"arg1": 1})", {} },
+            })
+            .run();
+
+        // Tool call with multiple params (mixed types)
+        tst.test(
+               "Multi-arg call</think>\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"magic_int\">\n"
+               "<｜DSML｜parameter name=\"ref\" string=\"false\">42</｜DSML｜parameter>\n"
+               "<｜DSML｜parameter name=\"name\" string=\"true\">foo bar</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ magic_int_tool })
+            .expect_reasoning("Multi-arg call")
+            .expect_tool_calls({
+                { "magic_int", R"({"ref": 42, "name": "foo bar"})", {} },
+            })
+            .run();
+
+        // Continuation tests
+        tst.test("world!\nWhat's up?")
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .enable_thinking(true)
+            .messages({ message_user, message_assist_prefill_content })
+            .add_generation_prompt(false)
+            .continue_final_message(COMMON_CHAT_CONTINUATION_CONTENT)
+            .expect_reasoning("I'm thinking")
+            .expect_content("Hello, world!\nWhat's up?")
+            .run();
+
+        tst.test(" thinking</think>Hello, world!\nWhat's up?")
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .enable_thinking(true)
+            .messages({ message_user, message_assist_prefill_reasoning })
+            .add_generation_prompt(false)
+            .continue_final_message(COMMON_CHAT_CONTINUATION_REASONING)
+            .expect_reasoning("I'm thinking")
+            .expect_content("Hello, world!\nWhat's up?")
+            .run();
+    }
+
     // GLM-4.6 tests - format: <tool_call>function_name\n<arg_key>...</arg_key>\n<arg_value>...</arg_value>\n</tool_call>
     {
         auto tst = peg_tester("models/templates/GLM-4.6.jinja", detailed_debug);
@@ -5918,6 +6053,144 @@ static void test_developer_role_to_system_workaround() {
     }
 }
 
+// Verify reasoning-trace retention rules in the DeepSeek-V4 template:
+// all traces are retained unless drop_thinking is true AND the conversation
+// has no tool calls, in which case only the last (after-final-user) trace is
+// kept and earlier ones are dropped.
+static void test_deepseek_v4_thinking_retention() {
+    LOG_DBG("%s\n", __func__);
+
+    auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja");
+
+    common_chat_msg user_q1; user_q1.role = "user"; user_q1.content = "Question 1";
+    common_chat_msg user_q2; user_q2.role = "user"; user_q2.content = "Question 2";
+    common_chat_msg asst_a1 = simple_assist_msg("Answer 1", "thinking A1");
+    common_chat_msg asst_a2 = simple_assist_msg("Answer 2", "thinking A2");
+
+    common_chat_msg tool_assist = message_with_tool_calls("special_function", "{\"arg1\": 1}");
+    common_chat_msg tool_result; tool_result.role = "tool";
+    tool_result.tool_name = "special_function"; tool_result.tool_call_id = "0"; tool_result.content = "result";
+
+    // The template uses U+FF5C as the role separator and literal think tags
+    // for the reasoning block.
+    const std::string asst_marker   = "<\xef\xbd\x9c" "Assistant" "\xef\xbd\x9c>";
+    // Built via concatenation so the thinking tokens are not interpreted by
+    // tooling processing this source file.
+    const std::string think_start   = "<" "think" ">";
+    const std::string think_end     = "</" "think" ">";
+
+    const std::string think_a1      = asst_marker + think_start + "thinking A1" + think_end;
+    const std::string think_a2      = asst_marker + think_start + "thinking A2" + think_end;
+    const std::string asst_no_think = asst_marker + think_end;
+
+    auto render = [&](const std::vector<common_chat_msg> & messages, bool drop_thinking) {
+        common_chat_templates_inputs inputs;
+        inputs.messages = messages;
+        inputs.add_generation_prompt = false;
+        inputs.chat_template_kwargs["thinking"]     = "true";
+        inputs.chat_template_kwargs["drop_thinking"] = drop_thinking ? "true" : "false";
+        return common_chat_templates_apply(tmpls.get(), inputs).prompt;
+    };
+
+    // No tools, drop_thinking=false: all reasoning is retained.
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ false);
+        assert_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+    }
+
+    // No tools, drop_thinking=true: only the last reasoning trace is kept,
+    // earlier ones are dropped (the assistant block emits just the end token).
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ true);
+        assert_not_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+        // The dropped assistant turn still opens with the marker + bare end token.
+        assert_contains(prompt, asst_no_think + "Answer 1");
+    }
+
+    // Single assistant turn, drop_thinking=true: the only trace is the last
+    // one, so it must be retained even with drop_thinking set.
+    {
+        auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ true);
+        assert_contains(prompt, think_a1);
+    }
+
+    // Single assistant turn, drop_thinking=false: reasoning is retained.
+    {
+        auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ false);
+        assert_contains(prompt, think_a1);
+    }
+
+    // With tool calls, drop_thinking=true: tool presence forces all reasoning
+    // to be retained, including the pre-tool-call trace.
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 },
+                             /* drop_thinking = */ true);
+        assert_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+    }
+
+    // With tool calls, drop_thinking=false: all reasoning retained.
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 },
+                             /* drop_thinking = */ false);
+        assert_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+    }
+}
+
+// Verify that consecutive tool results are rendered in the tool call order of the
+// preceding assistant message (matched by tool call id), as required by the reference
+// DeepSeek-V4 implementation.
+static void test_deepseek_v4_tool_result_ordering() {
+    LOG_DBG("%s\n", __func__);
+
+    auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja");
+
+    common_chat_msg user_q; user_q.role = "user"; user_q.content = "Question";
+
+    common_chat_msg assist_calls;
+    assist_calls.role = "assistant";
+    assist_calls.tool_calls.push_back({ "get_time",    "{\"city\": \"Paris\"}", "call_1" });
+    assist_calls.tool_calls.push_back({ "get_weather", "{\"city\": \"Paris\"}", "call_2" });
+
+    common_chat_msg time_result; time_result.role = "tool";
+    time_result.tool_name = "get_time"; time_result.tool_call_id = "call_1"; time_result.content = "12:00";
+    common_chat_msg weather_result; weather_result.role = "tool";
+    weather_result.tool_name = "get_weather"; weather_result.tool_call_id = "call_2"; weather_result.content = "sunny";
+
+    auto render = [&](const std::vector<common_chat_msg> & messages) {
+        common_chat_templates_inputs inputs;
+        inputs.messages = messages;
+        inputs.add_generation_prompt = false;
+        return common_chat_templates_apply(tmpls.get(), inputs).prompt;
+    };
+
+    // Results sent out of order are reordered to match the tool call order.
+    {
+        auto prompt = render({ user_q, assist_calls, weather_result, time_result });
+        assert_contains(prompt, "<tool_result>12:00</tool_result>\n\n<tool_result>sunny</tool_result>");
+    }
+
+    // Results already in call order stay put.
+    {
+        auto prompt = render({ user_q, assist_calls, time_result, weather_result });
+        assert_contains(prompt, "<tool_result>12:00</tool_result>\n\n<tool_result>sunny</tool_result>");
+    }
+
+    // Without tool call ids there is nothing to match against; order is preserved.
+    {
+        auto no_id_calls = assist_calls;
+        no_id_calls.tool_calls[0].id = "";
+        no_id_calls.tool_calls[1].id = "";
+        auto no_id_weather = weather_result; no_id_weather.tool_call_id = "";
+        auto no_id_time    = time_result;    no_id_time.tool_call_id = "";
+        auto prompt = render({ user_q, no_id_calls, no_id_weather, no_id_time });
+        assert_contains(prompt, "<tool_result>sunny</tool_result>\n\n<tool_result>12:00</tool_result>");
+    }
+}
+
 static void test_reasoning_budget_tokens_per_request() {
     LOG_DBG("%s\n", __func__);
     // Use Qwen3 template which has <think>...</think> reasoning markers.
@@ -6139,6 +6412,8 @@ int main(int argc, char ** argv) {
         test_tools_oaicompat_json_conversion();
         test_convert_responses_to_chatcmpl();
         test_developer_role_to_system_workaround();
+        test_deepseek_v4_thinking_retention();
+        test_deepseek_v4_tool_result_ordering();
         test_template_generation_prompt();
         test_reasoning_budget_tokens_per_request();
         test_reasoning_budget_message_per_request();

tools/mtmd/mtmd-helper.cpp

@@ -238,6 +238,29 @@ struct decode_embd_batch {
     }
 };
 
+// Helper class to set non-causal attention via RAII
+class scope_non_causal {
+public:
+    scope_non_causal(llama_context * context, bool enabled) : context_(context), enabled_(enabled) {
+        if (enabled_) {
+            // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image
+            llama_set_causal_attn(context_, false);
+        }
+    }
+    ~scope_non_causal() {
+        if (enabled_) {
+            llama_set_causal_attn(context_, true);
+        }
+    }
+
+    scope_non_causal(const scope_non_causal &) = delete;
+    scope_non_causal & operator=(const scope_non_causal &) = delete;
+
+private:
+    llama_context * context_;
+    bool enabled_;
+};
+
 // Helper function for decoding an image whose embeddings have already been calculated
 int32_t mtmd_helper_decode_image_chunk(
         mtmd_context * ctx,
@@ -288,10 +311,7 @@ int32_t mtmd_helper_decode_image_chunk(
     }
 
     const bool use_non_causal = mtmd_decode_use_non_causal(ctx, chunk);
-    if (use_non_causal) {
-        llama_set_causal_attn(lctx, false);
-        // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image
-    }
+    const scope_non_causal non_causal(lctx, use_non_causal);
 
     while (i_batch < n_img_batches) { // split into batches
         int pos_offset = i_batch*n_batch;
@@ -304,9 +324,6 @@ int32_t mtmd_helper_decode_image_chunk(
         int32_t ret = llama_decode(lctx, batch_embd_view);
         if (ret != 0) {
             LOG_ERR("failed to decode %s\n", name);
-            if (use_non_causal) {
-                llama_set_causal_attn(lctx, true);
-            }
             return ret;
         }
 
@@ -314,9 +331,6 @@ int32_t mtmd_helper_decode_image_chunk(
             ret = callback(batch_embd_view, user_data);
             if (ret != 0) {
                 LOG_ERR("post-decode callback failed\n");
-                if (use_non_causal) {
-                    llama_set_causal_attn(lctx, true);
-                }
                 return ret;
             }
         }
@@ -329,9 +343,6 @@ int32_t mtmd_helper_decode_image_chunk(
     n_past += mtmd_input_chunk_get_n_pos(chunk);
     *new_n_past = n_past;
 
-    if (use_non_causal) {
-        llama_set_causal_attn(lctx, true);
-    }
     return 0;
 }
 

tools/ui/.gitignore

@@ -36,3 +36,7 @@ static/favicon*
 *storybook.log
 storybook-static
 *.code-workspace
+
+# Vitest browser mode failure artifacts
+.vitest-attachments/
+tests/**/__screenshots__/

tools/ui/.prettierignore

@@ -16,3 +16,6 @@ build/
 /build/
 /.svelte-kit/
 test-results
+
+# Vendored third party sources, kept byte identical to upstream
+src/lib/vendors/

tools/ui/eslint.config.js

@@ -59,7 +59,8 @@ export default ts.config(
 			'.svelte-kit/**',
 			'test-results/**',
 			'.storybook/**/*',
-			'src/lib/services/sandbox-worker.js'
+			'src/lib/services/sandbox-worker.js',
+			'src/lib/vendors/**'
 		]
 	},
 	storybook.configs['flat/recommended']

tools/ui/scripts/vite-plugin-nerdamer.ts

@@ -0,0 +1,49 @@
+import { build } from 'esbuild';
+import { dirname, resolve } from 'path';
+import { fileURLToPath } from 'url';
+import type { Plugin } from 'vite';
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+
+const VENDORS_DIR = resolve(__dirname, '../src/lib/vendors');
+const VIRTUAL_ID = 'virtual:nerdamer';
+const RESOLVED_ID = '\0' + VIRTUAL_ID;
+
+/**
+ * Bundle the vendored nerdamer-prime source into a minified IIFE string,
+ * exposed as the `virtual:nerdamer` module. Flags mirror the upstream
+ * build (esbuild --bundle --minify --format=iife --global-name=nerdamer),
+ * so only human readable source lives in the repo and minification is a
+ * build artifact. Vendored under src/lib/vendors/, upstream snapshot:
+ * https://github.com/together-science/nerdamer-prime/commit/1936145f8af306ec0d883b9bfd7730aedd175c24
+ */
+export function nerdamerPlugin(): Plugin {
+	let bundled: string | null = null;
+
+	return {
+		name: 'llamacpp:nerdamer',
+		resolveId(id) {
+			return id === VIRTUAL_ID ? RESOLVED_ID : undefined;
+		},
+		async load(id) {
+			if (id !== RESOLVED_ID) return undefined;
+			if (bundled === null) {
+				const result = await build({
+					entryPoints: [resolve(VENDORS_DIR, 'nerdamer-prime/all.js')],
+					bundle: true,
+					minify: true,
+					format: 'iife',
+					globalName: 'nerdamer',
+					alias: {
+						'big-integer': resolve(VENDORS_DIR, 'big-integer/BigInteger.js'),
+						'decimal.js': resolve(VENDORS_DIR, 'decimal.js/decimal.js')
+					},
+					write: false,
+					logLevel: 'silent'
+				});
+				bundled = result.outputFiles[0].text;
+			}
+			return `export default ${JSON.stringify(bundled)};`;
+		}
+	};
+}

tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddDropdown.svelte

@@ -71,7 +71,9 @@
 
 <div class="flex items-center gap-1 {className}">
 	<DropdownMenu.Root bind:open={dropdownOpen}>
-		<Tooltip.Root>
+		<!-- ignoreNonKeyboardFocus prevents the tooltip from flashing when the
+		     menu closes and focus returns to the trigger -->
+		<Tooltip.Root ignoreNonKeyboardFocus>
 			<Tooltip.Trigger>
 				{#snippet child({ props })}
 					<DropdownMenu.Trigger

tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddReasoningSubmenu.svelte

@@ -5,18 +5,18 @@
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { useReasoningMenu } from '$lib/hooks/use-reasoning-menu.svelte';
 
-	let subOpen = $state(false);
-
 	const reasoning = useReasoningMenu();
 </script>
 
 {#if reasoning.modelSupportsThinking}
-	<DropdownMenu.Sub bind:open={subOpen}>
+	<DropdownMenu.Sub>
 		<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
 			{#if reasoning.thinkingEnabled}
 				<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-amber-400" />
-			{:else}
+			{:else if reasoning.isOff}
 				<LightbulbOff class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
+			{:else}
+				<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
 			{/if}
 
 			<span
@@ -27,7 +27,7 @@
 				Reasoning
 
 				<span class="capitalize text-muted-foreground">
-					{reasoning.thinkingEnabled ? reasoning.currentEffort : 'off'}
+					{reasoning.currentEffort}
 				</span>
 			</span>
 		</DropdownMenu.SubTrigger>
@@ -37,14 +37,13 @@
 		>
 			{#each reasoning.levels as level (level.value)}
 				{@const tokenLabel = reasoning.tokenLabel(level)}
-				<button
-					type="button"
-					class="flex w-full cursor-pointer items-center gap-3 rounded-md px-2 py-1.75 text-left text-sm transition-colors hover:bg-accent"
-					class:bg-accent={reasoning.isSelected(level)}
-					onclick={() => {
-						reasoning.select(level);
-						subOpen = false;
-					}}
+				<DropdownMenu.Item
+					class="flex w-full cursor-pointer items-center gap-3 rounded-md px-2 py-1.75 text-left text-sm transition-colors hover:bg-accent {reasoning.isSelected(
+						level
+					)
+						? 'bg-accent'
+						: ''}"
+					onclick={() => reasoning.select(level)}
 				>
 					{#if reasoning.isSelected(level)}
 						<Check class="{ICON_CLASS_DEFAULT} shrink-0 text-foreground" />
@@ -70,7 +69,7 @@
 							</Tooltip.Content>
 						</Tooltip.Root>
 					{/if}
-				</button>
+				</DropdownMenu.Item>
 			{/each}
 		</DropdownMenu.SubContent>
 	</DropdownMenu.Sub>

tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte

@@ -116,14 +116,16 @@
 
 							{#if reasoning.thinkingEnabled}
 								<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-amber-400" />
-							{:else}
+							{:else if reasoning.isOff}
 								<LightbulbOff class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
+							{:else}
+								<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
 							{/if}
 
 							<span class="flex-1">Reasoning</span>
 
 							<span class="text-xs capitalize text-muted-foreground">
-								{reasoning.thinkingEnabled ? reasoning.currentEffort : 'off'}
+								{reasoning.currentEffort}
 							</span>
 						</Collapsible.Trigger>
 

tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormReasoningEffortSubmenu.svelte

@@ -1,127 +0,0 @@
-<script lang="ts">
-	import { Check, Info, Lightbulb, LightbulbOff } from '@lucide/svelte';
-	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
-	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { ReasoningEffort, MessageRole } from '$lib/enums';
-	import { REASONING_EFFORT_TOKENS } from '$lib/constants/reasoning-effort-tokens';
-	import { REASONING_EFFORT_LEVELS } from '$lib/constants/reasoning-effort';
-	import type { ReasoningEffortLevel } from '$lib/types';
-	import { DIALOG_SUBMENU_CONTENT, ICON_CLASS_DEFAULT } from '$lib/constants/css-classes';
-	import {
-		modelsStore,
-		checkModelSupportsThinking,
-		supportsThinking,
-		propsCacheVersion,
-		loadedModelIds
-	} from '$lib/stores/models.svelte';
-	import { chatStore } from '$lib/stores/chat.svelte';
-	import { conversationsStore, activeMessages } from '$lib/stores/conversations.svelte';
-	import { isRouterMode } from '$lib/stores/server.svelte';
-	import type { DatabaseMessage } from '$lib/types/database';
-
-	let thinkingEnabled = $derived(conversationsStore.getThinkingEnabled());
-	let currentEffort = $derived(conversationsStore.getReasoningEffort());
-	let isOff = $derived(!thinkingEnabled);
-	let subOpen = $state(false);
-
-	// Get conversation model from message history
-	let conversationModel = $derived(
-		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
-	);
-
-	let modelSupportsThinkingFromMessages = $derived.by(() => {
-		const modelId = isRouterMode() ? modelsStore.selectedModelName || conversationModel : null;
-		if (!modelId) return false;
-
-		const messages = conversationsStore.activeMessages;
-
-		return messages.some(
-			(m: DatabaseMessage) =>
-				m.role === MessageRole.ASSISTANT && m.model === modelId && !!m.reasoningContent
-		);
-	});
-
-	let modelSupportsThinking = $derived.by(() => {
-		loadedModelIds();
-		propsCacheVersion();
-
-		if (isRouterMode()) {
-			const modelId = modelsStore.selectedModelName || conversationModel;
-			return checkModelSupportsThinking(modelId ?? '') || modelSupportsThinkingFromMessages;
-		}
-
-		return supportsThinking() || modelSupportsThinkingFromMessages;
-	});
-
-	function isSelected(item: ReasoningEffortLevel): boolean {
-		if (item.isOff) return isOff;
-
-		return thinkingEnabled && currentEffort === item.value;
-	}
-
-	function handleSelection(item: ReasoningEffortLevel) {
-		if (item.isOff) {
-			conversationsStore.setThinkingEnabled(false);
-		} else {
-			conversationsStore.setThinkingEnabled(true);
-			conversationsStore.setReasoningEffort(item.value as ReasoningEffort);
-		}
-		subOpen = false;
-	}
-</script>
-
-{#if modelSupportsThinking}
-	<DropdownMenu.Sub bind:open={subOpen}>
-		<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
-			{#if thinkingEnabled}
-				<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-amber-400" />
-			{:else}
-				<LightbulbOff class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
-			{/if}
-
-			<span class="flex-1">Thinking</span>
-
-			{#if thinkingEnabled}
-				<span class="text-xs text-muted-foreground">{currentEffort}</span>
-			{:else}
-				<span class="text-xs text-muted-foreground">off</span>
-			{/if}
-		</DropdownMenu.SubTrigger>
-
-		<DropdownMenu.SubContent class={DIALOG_SUBMENU_CONTENT}>
-			{#each REASONING_EFFORT_LEVELS as level (level.value)}
-				<button
-					type="button"
-					class="flex w-full cursor-pointer items-center gap-2"
-					class:bg-accent={isSelected(level)}
-					onclick={() => handleSelection(level)}
-				>
-					<span class="flex-1 text-left">{level.label}</span>
-
-					{#if !level.isOff}
-						<span class="text-[11px] text-muted-foreground opacity-60">
-							{REASONING_EFFORT_TOKENS[level.value] === -1
-								? 'Unlimited'
-								: `Max ${REASONING_EFFORT_TOKENS[level.value].toLocaleString()} tokens`}
-						</span>
-					{/if}
-
-					{#if level.hasInfo}
-						<Tooltip.Root>
-							<Tooltip.Trigger>
-								<Info class="h-3.5 w-3.5 shrink-0 text-muted-foreground" />
-							</Tooltip.Trigger>
-							<Tooltip.Content side="left">
-								<p>Maximum thinking effort with extended context usage</p>
-							</Tooltip.Content>
-						</Tooltip.Root>
-					{/if}
-
-					{#if isSelected(level)}
-						<Check class="{ICON_CLASS_DEFAULT} shrink-0 text-foreground" />
-					{/if}
-				</button>
-			{/each}
-		</DropdownMenu.SubContent>
-	</DropdownMenu.Sub>
-{/if}

tools/ui/src/lib/constants/reasoning-effort.ts

@@ -6,6 +6,7 @@ import type { ReasoningEffortLevel } from '$lib/types';
  * Keys match the ReasoningEffort enum values for type-safe lookups.
  */
 export const REASONING_EFFORT_LABELS: Record<string, string> = {
+	[ReasoningEffort.DEFAULT]: 'Default',
 	[ReasoningEffort.OFF]: 'Off',
 	[ReasoningEffort.LOW]: 'Low',
 	[ReasoningEffort.MEDIUM]: 'Medium',
@@ -14,7 +15,8 @@ export const REASONING_EFFORT_LABELS: Record<string, string> = {
 };
 
 export const REASONING_EFFORT_LEVELS: ReasoningEffortLevel[] = [
-	{ value: ReasoningEffort.OFF, label: 'Off', isOff: true },
+	{ value: ReasoningEffort.DEFAULT, label: 'Default' },
+	{ value: ReasoningEffort.OFF, label: 'Off' },
 	{ value: ReasoningEffort.LOW, label: 'Low' },
 	{ value: ReasoningEffort.MEDIUM, label: 'Medium' },
 	{ value: ReasoningEffort.HIGH, label: 'High' },

tools/ui/src/lib/constants/sandbox.ts

@@ -13,27 +13,44 @@ export const SANDBOX_EMPTY_OUTPUT = '(no output)';
 
 export const SANDBOX_TRUNCATION_NOTICE = '[output truncated]';
 
-export const SANDBOX_TOOL_DEFINITION: OpenAIToolDefinition = {
-	type: ToolCallType.FUNCTION,
-	function: {
-		name: SANDBOX_TOOL_NAME,
-		description:
-			'Execute JavaScript in a sandboxed browser worker (no DOM, no page access). ' +
-			'Top level await is supported. Use console.log to print intermediate values; ' +
-			'a top level return statement is captured as the result.',
-		parameters: {
-			type: JsonSchemaType.OBJECT,
-			properties: {
-				code: {
-					type: JsonSchemaType.STRING,
-					description: 'JavaScript source to execute'
+const NERDAMER_DESCRIPTION = `
+Symbolic/numeric math via \`nerdamer\` (pre-loaded, do not require, use it directly).
+nerdamer('diff(sin(x)/x,x)') or nerdamer.diff('sin(x)/x','x') → Expression; convert with .toString()/.text()/.toTeX(), or .evaluate() (→ still Expression, then .toString()).
+nerdamer(expr,{x:2}) substitutes only; chain .evaluate() or pass 'numer' for numeric result.
+solve(expr,var)→Symbol[]; solveEquations([eq1,..])→[[var,val],..] pairs.
+Functions: simplify/expand/factor(expr), diff(expr,var[,n]), integrate(expr,var), defint(expr,from,to,var), limit(expr,var,to), laplace(expr,t,s), ilt(expr,s,t), gcd/lcm(a,b), roots/coeffs/partfrac(expr,var), pfactor(n), numer/decimals/erf(expr), product/sum(expr,var,from,to), mean/median/stdev/variance(...vals).
+Object.keys(nerdamer).filter(k=>typeof nerdamer[k]==='function') lists all available functions. If you need a function not documented above, list them first — do not guess function names.`;
+
+/**
+ * Build the sandbox tool definition. When `includeSymbolicMath` is true,
+ * the description includes nerdamer API documentation; otherwise it
+ * describes a plain JavaScript sandbox.
+ */
+export function buildSandboxToolDefinition(includeSymbolicMath: boolean): OpenAIToolDefinition {
+	return {
+		type: ToolCallType.FUNCTION,
+		function: {
+			name: SANDBOX_TOOL_NAME,
+			description: includeSymbolicMath
+				? `Execute JS in a sandboxed browser worker (no DOM/page access). Top-level await ok; console.log for intermediates; top-level return is captured as result.${NERDAMER_DESCRIPTION}`
+				: 'Execute JS in a sandboxed browser worker (no DOM/page access). Top-level await ok; console.log for intermediates; top-level return is captured as result.',
+			parameters: {
+				type: JsonSchemaType.OBJECT,
+				properties: {
+					code: {
+						type: JsonSchemaType.STRING,
+						description: 'JavaScript source to execute'
+					},
+					timeout_ms: {
+						type: JsonSchemaType.NUMBER,
+						description: `Execution timeout in milliseconds, default ${SANDBOX_TIMEOUT_MS_DEFAULT}, max ${SANDBOX_TIMEOUT_MS_MAX}`
+					}
 				},
-				timeout_ms: {
-					type: JsonSchemaType.NUMBER,
-					description: `Execution timeout in milliseconds, default ${SANDBOX_TIMEOUT_MS_DEFAULT}, max ${SANDBOX_TIMEOUT_MS_MAX}`
-				}
-			},
-			required: ['code']
+				required: ['code']
+			}
 		}
-	}
-};
+	};
+}
+
+/** @deprecated Use {@link buildSandboxToolDefinition} instead. Kept for backward compatibility. */
+export const SANDBOX_TOOL_DEFINITION = buildSandboxToolDefinition(true);

tools/ui/src/lib/constants/settings-keys.ts

@@ -67,6 +67,7 @@ export const SETTINGS_KEYS = {
 	EXCLUDE_REASONING_FROM_CONTEXT: 'excludeReasoningFromContext',
 	SHOW_RAW_OUTPUT_SWITCH: 'showRawOutputSwitch',
 	JS_SANDBOX_ENABLED: 'jsSandboxEnabled',
+	SYMBOLIC_MATH_ENABLED: 'symbolicMathEnabled',
 	// PY_INTERPRETER_ENABLED: 'pyInterpreterEnabled',
 	CUSTOM_JSON: 'customJson',
 	CUSTOM_CSS: 'customCss'

tools/ui/src/lib/constants/settings-registry.ts

@@ -724,6 +724,15 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 					paramType: SyncableParameterType.BOOLEAN
 				}
 			},
+			{
+				key: SETTINGS_KEYS.SYMBOLIC_MATH_ENABLED,
+				label: 'Symbolic math (nerdamer)',
+				help: 'Pre-load nerdamer in the sandbox for symbolic computation: simplify, diff, integrate, solve, and more. Requires "JavaScript sandbox tool" to be enabled.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				dependsOn: SETTINGS_KEYS.JS_SANDBOX_ENABLED
+			},
 			{
 				key: SETTINGS_KEYS.CUSTOM_JSON,
 				label: 'Custom JSON',

tools/ui/src/lib/enums/reasoning-effort.enums.ts

@@ -3,6 +3,7 @@
  * These values are sent to the server and mapped to token budgets.
  */
 export enum ReasoningEffort {
+	DEFAULT = 'default',
 	OFF = 'off',
 	LOW = 'low',
 	MEDIUM = 'medium',

tools/ui/src/lib/hooks/use-reasoning-menu.svelte.ts

@@ -17,6 +17,7 @@ import { isRouterMode } from '$lib/stores/server.svelte';
 export interface UseReasoningMenuReturn {
 	readonly modelSupportsThinking: boolean;
 	readonly thinkingEnabled: boolean;
+	readonly isOff: boolean;
 	readonly currentEffort: ReasoningEffort;
 	readonly levels: ReasoningEffortLevel[];
 	isSelected(level: ReasoningEffortLevel): boolean;
@@ -59,8 +60,10 @@ export function useReasoningMenu(): UseReasoningMenuReturn {
 		return supportsThinking() || modelSupportsThinkingFromMessages;
 	});
 
-	const thinkingEnabled = $derived(conversationsStore.getThinkingEnabled());
 	const currentEffort = $derived(conversationsStore.getReasoningEffort());
+	const thinkingEnabled = $derived(
+		currentEffort !== ReasoningEffort.OFF && currentEffort !== ReasoningEffort.DEFAULT
+	);
 
 	return {
 		get modelSupportsThinking() {
@@ -69,6 +72,9 @@ export function useReasoningMenu(): UseReasoningMenuReturn {
 		get thinkingEnabled() {
 			return thinkingEnabled;
 		},
+		get isOff() {
+			return currentEffort === ReasoningEffort.OFF;
+		},
 		get currentEffort() {
 			return currentEffort;
 		},
@@ -76,20 +82,15 @@ export function useReasoningMenu(): UseReasoningMenuReturn {
 			return REASONING_EFFORT_LEVELS;
 		},
 		isSelected(level: ReasoningEffortLevel): boolean {
-			if (level.isOff) return !thinkingEnabled;
-			return thinkingEnabled && currentEffort === level.value;
+			return currentEffort === level.value;
 		},
 		tokenLabel(level: ReasoningEffortLevel): string | null {
-			if (level.isOff) return null;
+			if (level.value === ReasoningEffort.DEFAULT) return 'Model default';
 			const tokens = REASONING_EFFORT_TOKENS[level.value];
+			if (tokens === undefined) return null;
 			return tokens === -1 ? 'Unlimited' : `Max ${tokens.toLocaleString()} tokens`;
 		},
 		select(level: ReasoningEffortLevel): void {
-			if (level.isOff) {
-				conversationsStore.setThinkingEnabled(false);
-				return;
-			}
-			conversationsStore.setThinkingEnabled(true);
 			conversationsStore.setReasoningEffort(level.value as ReasoningEffort);
 		}
 	};

tools/ui/src/lib/services/chat.service.ts

@@ -271,10 +271,14 @@ export class ChatService {
 		const reasoningBudgetTokens =
 			enableThinking && reasoningEffort ? (REASONING_EFFORT_TOKENS[reasoningEffort] ?? -1) : -1;
 
-		requestBody.chat_template_kwargs = {
-			...(requestBody.chat_template_kwargs ?? {}),
-			enable_thinking: enableThinking
-		};
+		// an explicit user choice injects the kwarg, otherwise it is omitted so
+		// the server default applies (--reasoning flag or chat template)
+		if (enableThinking !== undefined) {
+			requestBody.chat_template_kwargs = {
+				...(requestBody.chat_template_kwargs ?? {}),
+				enable_thinking: enableThinking
+			};
+		}
 
 		if (reasoningBudgetTokens >= 0) {
 			requestBody.thinking_budget_tokens = reasoningBudgetTokens;

tools/ui/src/lib/services/index.ts

@@ -276,7 +276,7 @@ export { MCPService } from './mcp.service';
  * - **toolsStore**: Exposes the tool definition when the sandbox is enabled
  * - **agenticStore**: Dispatches ToolSource.FRONTEND calls here
  *
- * @see SANDBOX_TOOL_DEFINITION in constants/sandbox.ts - tool schema sent to the LLM
+ * @see buildSandboxToolDefinition in constants/sandbox.ts - tool schema sent to the LLM
  * @see agenticStore in stores/agentic.svelte.ts - tool dispatch
  */
 export { SandboxService } from './sandbox.service';

tools/ui/src/lib/services/sandbox-harness.ts

@@ -1,14 +1,25 @@
+import { NEWLINE } from '$lib/constants';
 import WORKER_SHIM from './sandbox-worker.js?raw';
 
+/**
+ * CSP for the harness document, inherited by the blob worker. connect-src
+ * falls back to default-src, removing network egress for model and vendored
+ * code. 'unsafe-eval' is required by the worker's AsyncFunction constructor,
+ * 'unsafe-inline' by the inline script below, worker-src by the blob worker.
+ */
+const HARNESS_CSP = `default-src 'none'; script-src 'unsafe-inline' 'unsafe-eval'; worker-src blob:`;
+
 /**
  * Harness loaded as srcdoc into a sandboxed iframe (allow-scripts only).
  * The opaque origin is the security boundary: no access to the app origin,
  * its storage or its API. The harness spawns a worker so model code never
  * runs on a main thread, which makes the parent timeout enforceable by
- * removing the iframe.
+ * removing the iframe. The prelude runs in the worker before the shim,
+ * exposing globals such as `nerdamer` to model code.
  */
-export const SANDBOX_HARNESS_HTML = `<!doctype html><script>
-const SHIM = ${JSON.stringify(WORKER_SHIM)};
+export function buildSandboxHarness(preludeJs: string): string {
+	return `<!doctype html><meta http-equiv="Content-Security-Policy" content="${HARNESS_CSP}"><script>
+const SHIM = ${JSON.stringify(preludeJs + NEWLINE + WORKER_SHIM)};
 addEventListener('message', (event) => {
 	const respond = (payload) => parent.postMessage(payload, '*');
 	let worker;
@@ -23,3 +34,4 @@ addEventListener('message', (event) => {
 	worker.postMessage({ code: event.data.code });
 });
 </script>`;
+}

tools/ui/src/lib/services/sandbox-worker.js

@@ -21,7 +21,9 @@ self.onmessage = async (event) => {
 	const reply = { logs, result: null, error: null };
 	try {
 		const AsyncFunction = Object.getPrototypeOf(async function () {}).constructor;
-		const value = await new AsyncFunction(event.data.code)();
+		// The prelude bundled ahead of this shim defines self.nerdamer,
+		// passed into the execution scope as the `nerdamer` parameter.
+		const value = await new AsyncFunction('nerdamer', event.data.code)(self.nerdamer);
 		if (value !== undefined) reply.result = fmt(value);
 	} catch (err) {
 		reply.error = err instanceof Error ? err.stack || err.message : String(err);

tools/ui/src/lib/services/sandbox.service.ts

@@ -7,9 +7,32 @@ import {
 	SANDBOX_TOOL_NAME,
 	SANDBOX_TRUNCATION_NOTICE
 } from '$lib/constants';
-import { SANDBOX_HARNESS_HTML } from './sandbox-harness';
+import { buildSandboxHarness } from './sandbox-harness';
+import { config } from '$lib/stores/settings.svelte';
 import type { ToolExecutionResult } from '$lib/types';
 
+/** Cached harnesses keyed by whether nerdamer is included. */
+const harnessCache: Record<string, string> = {};
+
+/**
+ * Build the sandbox harness. When symbolic math is enabled, loads the
+ * nerdamer prelude lazily; otherwise builds a plain harness with an empty
+ * prelude. Cached per variant so toggling the setting is instant.
+ */
+async function getHarness(): Promise<string> {
+	const enabled = !!config().symbolicMathEnabled;
+	const key = enabled ? 'nerdamer' : 'plain';
+	if (!harnessCache[key]) {
+		if (enabled) {
+			const { default: nerdamerJs } = await import('virtual:nerdamer');
+			harnessCache[key] = buildSandboxHarness(nerdamerJs);
+		} else {
+			harnessCache[key] = buildSandboxHarness('');
+		}
+	}
+	return harnessCache[key];
+}
+
 interface SandboxReply {
 	logs?: unknown;
 	result?: unknown;
@@ -45,20 +68,22 @@ export class SandboxService {
 	 * timeout or abort. Removing the iframe terminates the worker
 	 * at the browser level, so runaway code cannot outlive it.
 	 */
-	static executeTool(
+	static async executeTool(
 		toolName: string,
 		params: Record<string, unknown>,
 		signal?: AbortSignal
 	): Promise<ToolExecutionResult> {
 		if (toolName !== SANDBOX_TOOL_NAME) {
-			return Promise.resolve({ content: `Unknown frontend tool: ${toolName}`, isError: true });
+			return { content: `Unknown frontend tool: ${toolName}`, isError: true };
 		}
 
 		const code = typeof params.code === 'string' ? params.code : '';
 		if (!code) {
-			return Promise.resolve({ content: 'Missing required parameter: code', isError: true });
+			return { content: 'Missing required parameter: code', isError: true };
 		}
 
+		const harness = await getHarness();
+
 		const requested = Number(params.timeout_ms);
 		const timeoutMs =
 			Number.isFinite(requested) && requested > 0
@@ -69,7 +94,7 @@ export class SandboxService {
 			const iframe = document.createElement('iframe');
 			iframe.setAttribute('sandbox', 'allow-scripts');
 			iframe.style.display = 'none';
-			iframe.srcdoc = SANDBOX_HARNESS_HTML;
+			iframe.srcdoc = harness;
 
 			let settled = false;
 

tools/ui/src/lib/stores/chat.svelte.ts

@@ -2373,9 +2373,12 @@ class ChatStore {
 
 		if (currentConfig.excludeReasoningFromContext) apiOptions.excludeReasoningFromContext = true;
 
-		apiOptions.enableThinking = conversationsStore.getThinkingEnabled();
+		// an explicit reasoning choice overrides the server default, DEFAULT sends nothing
 		const effort = conversationsStore.getReasoningEffort();
-		if (effort !== ReasoningEffort.OFF) apiOptions.reasoningEffort = effort;
+		if (effort !== ReasoningEffort.DEFAULT) {
+			apiOptions.enableThinking = effort !== ReasoningEffort.OFF;
+			if (effort !== ReasoningEffort.OFF) apiOptions.reasoningEffort = effort;
+		}
 
 		if (hasValue(currentConfig.temperature))
 			apiOptions.temperature = Number(currentConfig.temperature);

tools/ui/src/lib/stores/conversations.svelte.ts

@@ -80,25 +80,17 @@ class ConversationsStore {
 	/** Whether the store has been initialized */
 	isInitialized = $state(false);
 
-	/** Global (non-conversation-specific) thinking toggle default, derived from reasoning effort */
-	pendingThinkingEnabled = $state(false);
-
 	/** Global (non-conversation-specific) reasoning effort default */
-	pendingReasoningEffort = $state<ReasoningEffort | ReasoningEffort.OFF>(
-		ConversationsStore.loadReasoningEffortDefault()
-	);
+	pendingReasoningEffort = $state<ReasoningEffort>(ConversationsStore.loadReasoningEffortDefault());
 
-	/** Last non-off reasoning effort, restored when re-enabling thinking globally */
-	private lastNonOffEffort: ReasoningEffort | null = null;
-
-	/** Load reasoning effort default from localStorage */
-	private static loadReasoningEffortDefault(): ReasoningEffort | ReasoningEffort.OFF {
-		if (typeof globalThis.localStorage === 'undefined') return ReasoningEffort.OFF;
+	/** Load reasoning effort default from localStorage, DEFAULT defers to the server */
+	private static loadReasoningEffortDefault(): ReasoningEffort {
+		if (typeof globalThis.localStorage === 'undefined') return ReasoningEffort.DEFAULT;
 		try {
 			const raw = localStorage.getItem(REASONING_EFFORT_DEFAULT_LOCALSTORAGE_KEY);
-			return (raw as ReasoningEffort | ReasoningEffort.OFF) || ReasoningEffort.OFF;
+			return (raw as ReasoningEffort) || ReasoningEffort.DEFAULT;
 		} catch {
-			return ReasoningEffort.OFF;
+			return ReasoningEffort.DEFAULT;
 		}
 	}
 
@@ -235,17 +227,10 @@ class ConversationsStore {
 		// servers without a per-conversation override to `mcpServers[i].enabled`,
 		// and only explicit toggles are stored on the conversation.
 
-		// Inherit global thinking/reasoning defaults into the new conversation
-		const thinkingEnabled = this.getThinkingEnabled();
-		conversation.thinkingEnabled = thinkingEnabled;
-		conversation.reasoningEffort =
-			this.pendingReasoningEffort === ReasoningEffort.OFF ? undefined : this.pendingReasoningEffort;
+		// Inherit the global reasoning default into the new conversation
+		conversation.reasoningEffort = this.pendingReasoningEffort;
 		await DatabaseService.updateConversation(conversation.id, {
-			thinkingEnabled,
-			reasoningEffort:
-				this.pendingReasoningEffort === ReasoningEffort.OFF
-					? undefined
-					: this.pendingReasoningEffort
+			reasoningEffort: this.pendingReasoningEffort
 		});
 
 		this.conversations = [conversation, ...this.conversations];
@@ -793,63 +778,21 @@ class ConversationsStore {
 		await this.setMcpServerOverride(serverId, undefined);
 	}
 
-	/**
-	 * Gets the effective thinking-enabled state for the active conversation.
-	 * Returns the conversation override if set, otherwise the global default.
-	 */
-	getThinkingEnabled(): boolean {
-		if (this.activeConversation) {
-			if (this.activeConversation.thinkingEnabled !== undefined) {
-				return this.activeConversation.thinkingEnabled;
-			}
-		}
-		return this.getReasoningEffort() !== ReasoningEffort.OFF;
-	}
-
-	/**
-	 * Sets the thinking-enabled state for the active conversation.
-	 * If no conversation exists, stores the global default.
-	 * @param enabled - The enabled state
-	 */
-	async setThinkingEnabled(enabled: boolean): Promise<void> {
-		if (!this.activeConversation) {
-			if (enabled) {
-				const effort = this.lastNonOffEffort ?? ReasoningEffort.LOW;
-				this.pendingReasoningEffort = effort;
-				this.saveReasoningEffortDefaults();
-			} else {
-				if (this.pendingReasoningEffort !== ReasoningEffort.OFF) {
-					this.lastNonOffEffort = this.pendingReasoningEffort;
-				}
-				this.pendingReasoningEffort = ReasoningEffort.OFF;
-				this.saveReasoningEffortDefaults();
-			}
-			return;
-		}
-
-		this.activeConversation = {
-			...this.activeConversation,
-			thinkingEnabled: enabled
-		};
-
-		await DatabaseService.updateConversation(this.activeConversation.id, {
-			thinkingEnabled: enabled
-		});
-
-		const convIndex = this.conversations.findIndex((c) => c.id === this.activeConversation!.id);
-		if (convIndex !== -1) {
-			this.conversations[convIndex].thinkingEnabled = enabled;
-			this.conversations = [...this.conversations];
-		}
-	}
-
 	/**
 	 * Gets the effective reasoning effort for the active conversation.
 	 * Returns the conversation override if set, otherwise the global default.
+	 * DEFAULT means no override is sent and the server decides.
 	 */
-	getReasoningEffort(): ReasoningEffort | ReasoningEffort.OFF {
+	getReasoningEffort(): ReasoningEffort {
 		if (this.activeConversation) {
-			return this.activeConversation.reasoningEffort ?? this.pendingReasoningEffort;
+			if (this.activeConversation.reasoningEffort !== undefined) {
+				return this.activeConversation.reasoningEffort;
+			}
+			// conversations created before the tri-state store an explicit
+			// opt-out only as thinkingEnabled = false
+			if (this.activeConversation.thinkingEnabled === false) {
+				return ReasoningEffort.OFF;
+			}
 		}
 		return this.pendingReasoningEffort;
 	}
@@ -857,7 +800,7 @@ class ConversationsStore {
 	/**
 	 * Sets the reasoning effort for the active conversation.
 	 * If no conversation exists, stores the global default.
-	 * @param effort - The effort level ('low' | 'medium' | 'high' | 'max')
+	 * @param effort - The effort level ('default' | 'off' | 'low' | 'medium' | 'high' | 'max')
 	 */
 	async setReasoningEffort(effort: ReasoningEffort): Promise<void> {
 		if (!this.activeConversation) {

tools/ui/src/lib/stores/tools.svelte.ts

@@ -5,7 +5,7 @@ import { HealthCheckStatus, JsonSchemaType, ToolCallType, ToolSource } from '$li
 import { config } from '$lib/stores/settings.svelte';
 import {
 	DISABLED_TOOL_KEYS_LOCALSTORAGE_KEY,
-	SANDBOX_TOOL_DEFINITION,
+	buildSandboxToolDefinition,
 	TOOL_GROUP_LABELS,
 	TOOL_SERVER_LABELS
 } from '$lib/constants';
@@ -143,7 +143,9 @@ class ToolsStore {
 	}
 
 	get frontendTools(): OpenAIToolDefinition[] {
-		return config().jsSandboxEnabled ? [SANDBOX_TOOL_DEFINITION] : [];
+		return config().jsSandboxEnabled
+			? [buildSandboxToolDefinition(!!config().symbolicMathEnabled)]
+			: [];
 	}
 
 	get customTools(): OpenAIToolDefinition[] {

tools/ui/src/lib/types/reasoning.ts

@@ -1,6 +1,5 @@
 export interface ReasoningEffortLevel {
 	value: string;
 	label: string;
-	isOff?: boolean;
 	hasInfo?: boolean;
 }

tools/ui/src/lib/vendors/big-integer/LICENSE

@@ -0,0 +1,24 @@
+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or
+distribute this software, either in source code form or as a compiled
+binary, for any purpose, commercial or non-commercial, and by any
+means.
+
+In jurisdictions that recognize copyright laws, the author or authors
+of this software dedicate any and all copyright interest in the
+software to the public domain. We make this dedication for the benefit
+of the public at large and to the detriment of our heirs and
+successors. We intend this dedication to be an overt act of
+relinquishment in perpetuity of all present and future rights to this
+software under copyright law.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
+
+For more information, please refer to <http://unlicense.org>

tools/ui/src/lib/vendors/decimal.js/LICENCE.md

@@ -0,0 +1,23 @@
+The MIT Licence.
+
+Copyright (c) 2025 Michael Mclaughlin
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+'Software'), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

tools/ui/src/lib/vendors/nerdamer-prime/Extra.js

@@ -0,0 +1,926 @@
+/*
+ * Author : Martin Donk
+ * Website : http://www.nerdamer.com
+ * Email : martin.r.donk@gmail.com
+ * License : MIT
+ * Source : https://github.com/jiggzson/nerdamer
+ */
+
+// Type imports for JSDoc ======================================================
+// These typedefs provide type aliases for the interfaces defined in index.d.ts.
+// They enable proper type checking when working with the classes defined in this file.
+//
+// Usage patterns:
+// - For return types: @returns {NerdamerSymbolType}
+// - For parameters: @param {NerdamerSymbolType} symbol
+// - For variable declarations: /** @type {NerdamerSymbolType} */
+//
+// Note: When casting local class instances to interface types, use the pattern:
+//   /** @type {InterfaceType} */ (/** @type {unknown} */ (localInstance))
+// This is needed because TypeScript sees local classes and interfaces as separate types.
+
+/**
+ * Core type aliases from index.d.ts
+ *
+ * @typedef {import('./index').NerdamerCore.NerdamerSymbol} NerdamerSymbolType
+ *
+ * @typedef {import('./index').NerdamerCore.Frac} FracType
+ *
+ * @typedef {import('./index').NerdamerCore.Vector} VectorType
+ *
+ * @typedef {import('./index').NerdamerCore.Matrix} MatrixType
+ *
+ * @typedef {import('./index').NerdamerCore.Parser} ParserType
+ *
+ * @typedef {import('./index').NerdamerCore.Settings} SettingsType
+ *
+ * @typedef {import('./index').NerdamerExpression} ExpressionType
+ *
+ * @typedef {typeof import('./index')} NerdamerType
+ *
+ * @typedef {import('./index').NerdamerCore.Utils} UtilsInterface
+ *
+ * @typedef {import('./index').NerdamerCore.Math2} Math2Interface
+ *
+ * @typedef {import('./index').NerdamerCore.Core} CoreType
+ *
+ * @typedef {import('./index').ExpressionParam} ExpressionParam
+ *
+ * @typedef {import('./index').ArithmeticOperand} ArithmeticOperand
+ *
+ * @typedef {import('./index').NerdamerCore.AlgebraModule} AlgebraModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.PartFracSubModule} PartFracSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.CalculusModule} CalculusModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.ExtraModule} ExtraModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.LaPlaceSubModule} LaPlaceSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.StatisticsSubModule} StatisticsSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.UnitsSubModule} UnitsSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.DecomposeResultObject} DecomposeResultType
+ */
+
+// Check if nerdamer exists globally (browser) or needs to be required (Node.js)
+let nerdamer = typeof globalThis !== 'undefined' && globalThis.nerdamer ? globalThis.nerdamer : undefined;
+if (typeof module !== 'undefined' && nerdamer === undefined) {
+    nerdamer = require('./nerdamer.core.js');
+    require('./Calculus');
+    require('./Algebra');
+}
+
+/** @returns {ExtraModuleType} */
+(function initExtraModule() {
+    /** @type {CoreType} */
+    const core = nerdamer.getCore();
+    /** @type {ParserType} */
+    const _ = core.PARSER;
+    const {
+        NerdamerSymbol,
+        Vector: _Vector,
+        /** @type {AlgebraModuleType} */
+        Algebra,
+        /** @type {CalculusModuleType} */
+        Calculus,
+    } = core;
+    const { format, isVector, isArray, isSymbol } = core.Utils;
+    const { S, EX: _EX, CP, PL, CB, FN } = core.groups;
+    core.Settings.Laplace_integration_depth = 40;
+
+    /**
+     * Check if a symbol's power is itself a symbol with group S or CB
+     *
+     * @param {NerdamerSymbolType} sym
+     * @returns {boolean}
+     */
+    function hasPowerGroupSOrCB(sym) {
+        return isSymbol(sym.power) && (sym.power.group === S || sym.power.group === CB);
+    }
+
+    /**
+     * Finds a function by name within this symbol's tree.
+     *
+     * @this {NerdamerSymbolType}
+     * @param {string} fname The function name to search for
+     * @returns {NerdamerSymbolType | undefined} The found function symbol clone, or undefined if not found
+     */
+    NerdamerSymbol.prototype.findFunction = function findFunction(fname) {
+        // This is what we're looking for
+        if (this.group === FN && this.fname === fname) {
+            return this.clone();
+        }
+        let found;
+        if (this.symbols) {
+            for (const x in this.symbols) {
+                if (!Object.hasOwn(this.symbols, x)) {
+                    continue;
+                }
+                found = this.symbols[x].findFunction(fname);
+                if (found) {
+                    break;
+                }
+            }
+        }
+
+        return found;
+    };
+
+    /** @type {ExtraModuleType} */
+    const __ = (core.Extra = {
+        version: '1.4.2',
+        // http://integral-table.com/downloads/LaplaceTable.pdf
+        // Laplace assumes all coefficients to be positive
+        LaPlace: {
+            // Using: integral_0^oo f(t)*e^(-s*t) dt
+            /**
+             * @param {NerdamerSymbolType} symbol
+             * @param {NerdamerSymbolType | string} t
+             * @param {NerdamerSymbolType | string} s
+             * @returns {NerdamerSymbolType}
+             */
+            transform(symbol, t, s) {
+                /** @type {NerdamerSymbolType} */
+                symbol = symbol.clone();
+
+                t = t.toString();
+                // First try a lookup for a speed boost
+                symbol = NerdamerSymbol.unwrapSQRT(symbol, true);
+                /** @type {NerdamerSymbolType} */
+                let retval;
+                const coeff = symbol.stripVar(t);
+                const g = symbol.group;
+
+                symbol = /** @type {NerdamerSymbolType} */ (_.divide(symbol, coeff.clone()));
+
+                if (symbol.isConstant() || !symbol.contains(t, true)) {
+                    retval = _.parse(format('({0})/({1})', symbol, s));
+                } else if (g === S && core.Utils.isInt(symbol.power)) {
+                    const n = String(symbol.power);
+                    retval = _.parse(format('factorial({0})/({1})^({0}+1)', n, s));
+                } else if (symbol.group === S && symbol.power.equals(1 / 2)) {
+                    retval = _.parse(format('sqrt(pi)/(2*({0})^(3/2))', s));
+                } else if (symbol.isComposite()) {
+                    retval = new NerdamerSymbol(0);
+                    symbol.each(x => {
+                        retval = /** @type {NerdamerSymbolType} */ (_.add(retval, __.LaPlace.transform(x, t, s)));
+                    }, true);
+                } else if (symbol.isE() && hasPowerGroupSOrCB(symbol)) {
+                    const a = /** @type {NerdamerSymbolType} */ (symbol.power).stripVar(t);
+                    retval = _.parse(format('1/(({1})-({0}))', a, s));
+                } else {
+                    const fns = ['sin', 'cos', 'sinh', 'cosh'];
+                    // Support for symbols in fns with arguments in the form a*t or n*t where a = symbolic and n = Number
+                    if (
+                        symbol.group === FN &&
+                        fns.indexOf(symbol.fname) !== -1 &&
+                        (symbol.args[0].group === S || symbol.args[0].group === CB)
+                    ) {
+                        const a = symbol.args[0].stripVar(t);
+
+                        switch (symbol.fname) {
+                            case 'sin':
+                                retval = _.parse(format('({0})/(({1})^2+({0})^2)', a, s));
+                                break;
+                            case 'cos':
+                                retval = _.parse(format('({1})/(({1})^2+({0})^2)', a, s));
+                                break;
+                            case 'sinh':
+                                retval = _.parse(format('({0})/(({1})^2-({0})^2)', a, s));
+                                break;
+                            case 'cosh':
+                                retval = _.parse(format('({1})/(({1})^2-({0})^2)', a, s));
+                                break;
+                        }
+                    } else {
+                        // Try to integrate for a solution
+                        // we need at least the Laplace integration depth
+                        const depthIsLower = core.Settings.integration_depth < core.Settings.Laplace_integration_depth;
+
+                        let savedIntegrationDepth;
+                        if (depthIsLower) {
+                            savedIntegrationDepth = core.Settings.integration_depth; // Save the depth
+                            core.Settings.integration_depth = core.Settings.Laplace_integration_depth; // Transforms need a little more room
+                        }
+
+                        core.Utils.block(
+                            'PARSE2NUMBER',
+                            () => {
+                                const u = t;
+                                const sym = symbol.sub(t, u);
+                                const integrationExpr = _.parse(`e^(-${s}*${u})*${sym}`);
+                                retval = Calculus.integrate(integrationExpr, u);
+                                if (retval.hasIntegral?.()) {
+                                    retval = _.symfunction('laplace', [symbol, _.parse(String(t)), _.parse(String(s))]);
+                                    return;
+                                }
+                                //                                _.error('Unable to compute transform');
+                                retval = retval.sub(t, 0);
+                                retval = /** @type {NerdamerSymbolType} */ (
+                                    _.expand(_.multiply(retval, new NerdamerSymbol(-1)))
+                                );
+                                retval = retval.sub(u, t);
+                            },
+                            false
+                        );
+
+                        retval = /** @type {NerdamerSymbolType} */ (
+                            core.Utils.block('PARSE2NUMBER', () => _.parse(retval), true)
+                        );
+
+                        if (depthIsLower) // Put the integration depth as it was
+                        {
+                            core.Settings.integration_depth = savedIntegrationDepth;
+                        }
+                    }
+                }
+
+                return /** @type {NerdamerSymbolType} */ (_.multiply(retval, coeff));
+            },
+            /**
+             * @param {NerdamerSymbolType} symbol
+             * @param {NerdamerSymbolType | string} s_
+             * @param {NerdamerSymbolType | string} t
+             * @returns {NerdamerSymbolType}
+             */
+            inverse(symbol, s_, t) {
+                const inputSymbol = symbol.clone();
+                return core.Utils.block(
+                    'POSITIVE_MULTIPLIERS',
+                    () => {
+                        /** @type {NerdamerSymbolType | undefined} */
+                        let retval;
+                        // Expand and get partial fractions
+                        if (symbol.group === CB) {
+                            symbol = /** @type {NerdamerSymbolType} */ (
+                                /** @type {PartFracSubModuleType} */ (Algebra.PartFrac).partfrac(
+                                    /** @type {NerdamerSymbolType} */ (_.expand(symbol)),
+                                    s_
+                                )
+                            );
+                        }
+
+                        if (symbol.group === S || symbol.group === CB || symbol.isComposite()) {
+                            /** @type {number | FracType} */
+                            let p;
+                            /** @type {FracType} */
+                            let denP;
+                            /** @type {NerdamerSymbolType} */
+                            let a;
+                            /** @type {NerdamerSymbolType | string} */
+                            let b;
+                            /** @type {NerdamerSymbolType} */
+                            let d;
+                            /** @type {string} */
+                            let exp;
+                            /** @type {DecomposeResultType} */
+                            let f2;
+                            /** @type {string | number} */
+                            let fact;
+                            // Remove the multiplier
+                            const m = symbol.multiplier.clone();
+                            symbol.toUnitMultiplier();
+                            // Get the numerator and denominator
+                            let num = symbol.getNum();
+                            const den = symbol.getDenom().toUnitMultiplier();
+
+                            // TODO: Make it so factor doesn't destroy pi
+                            // num = core.Algebra.Factor.factor(symbol.getNum());
+                            // den = core.Algebra.Factor.factor(symbol.getDenom().invert(null, true));
+
+                            if (den.group === CP || den.group === PL) {
+                                denP = /** @type {FracType} */ (den.power.clone());
+                                den.toLinear();
+                            } else {
+                                denP = new core.Frac(1);
+                            }
+
+                            // Convert s to a string
+                            const s = s_.toString();
+                            // Split up the denominator if in the form ax+b
+                            /** @type {DecomposeResultType} */
+                            const f = core.Utils.decompose_fn(den, s, true);
+                            // Move the multiplier to the numerator
+                            /** @type {DecomposeResultType} */
+                            const _fe = core.Utils.decompose_fn(
+                                /** @type {NerdamerSymbolType} */ (_.expand(num.clone())),
+                                s,
+                                true
+                            );
+                            num.multiplier = num.multiplier.multiply(m);
+
+                            const finalize = function () {
+                                // Put back the numerator
+                                retval = /** @type {NerdamerSymbolType} */ (_.multiply(retval, num));
+                                retval.multiplier = retval.multiplier.multiply(symbol.multiplier);
+                                // Put back a
+                                retval = /** @type {NerdamerSymbolType} */ (_.divide(retval, f.a));
+                            };
+
+                            // Store the parts in variables for easy recognition
+                            // check if in the form t^n where n = integer
+                            if (
+                                (den.group === S || den.group === CB) &&
+                                f.x.value === s &&
+                                f.b.equals(0) &&
+                                core.Utils.isInt(f.x.power)
+                            ) {
+                                p = /** @type {number} */ (/** @type {unknown} */ (f.x.power)) - 1;
+                                fact = core.Math2.factorial(p);
+                                //  N!/s^(n-1)
+                                retval = /** @type {NerdamerSymbolType} */ (
+                                    _.divide(_.pow(_.parse(String(t)), new NerdamerSymbol(p)), new NerdamerSymbol(fact))
+                                );
+                                // Wrap it up
+                                finalize();
+                            } else if (den.group === CP && denP.equals(1)) {
+                                if (f.x.group === core.groups.PL && Algebra.degree(den).equals(2)) {
+                                    // Possibly in the form 1/(s^2+2*s+1)
+                                    // Try factoring to get it in a more familiar form{
+                                    // Apply inverse of F(s-a)
+                                    /**
+                                     * @type {{
+                                     *     f: NerdamerSymbolType;
+                                     *     a: NerdamerSymbolType;
+                                     *     h: NerdamerSymbolType;
+                                     *     c?: NerdamerSymbolType;
+                                     * }}
+                                     */
+                                    const completed = Algebra.sqComplete(den, s);
+                                    const u = core.Utils.getU(den);
+                                    // Get a for the function above
+                                    a = core.Utils.decompose_fn(completed.a, s, true).b;
+                                    const tf = __.LaPlace.inverse(
+                                        _.parse(`1/((${u})^2+(${completed.c}))`),
+                                        u,
+                                        String(t)
+                                    );
+                                    retval = /** @type {NerdamerSymbolType} */ (
+                                        _.multiply(tf, _.parse(`(${m})*e^(-(${a})*(${t}))`))
+                                    );
+                                    // A/(b*s-c) -> ae^(-bt)
+                                } else if (f.x.isLinear() && !num.contains(s)) {
+                                    t = /** @type {NerdamerSymbolType | string} */ (
+                                        _.divide(_.parse(String(t)), f.a.clone())
+                                    );
+
+                                    // Don't add factorial of one or zero
+                                    p = /** @type {number} */ (/** @type {unknown} */ (denP)) - 1;
+                                    fact = p === 0 || p === 1 ? '1' : `(${denP}-1)!`;
+                                    retval = _.parse(
+                                        format(
+                                            '(({0})^({3}-1)*e^(-(({2})*({0}))/({1})))/(({4})*({1})^({3}))',
+                                            t,
+                                            f.a,
+                                            f.b,
+                                            denP,
+                                            fact
+                                        )
+                                    );
+                                    // Wrap it up
+                                    finalize();
+                                } else if (f.x.group === S && f.x.power.equals(2)) {
+                                    if (num.contains(s)) {
+                                        // A*s/(b*s^2+c^2)
+                                        a = new NerdamerSymbol(1);
+                                        if (num.group === CB) {
+                                            /** @type {NerdamerSymbolType} */
+                                            let newNum = new NerdamerSymbol(1);
+                                            num.each(x => {
+                                                if (x.contains(s)) {
+                                                    newNum = /** @type {NerdamerSymbolType} */ (_.multiply(newNum, x));
+                                                } else {
+                                                    a = /** @type {NerdamerSymbolType} */ (_.multiply(a, x));
+                                                }
+                                            });
+                                            num = newNum;
+                                        }
+
+                                        // We need more information about the denominator to decide
+                                        f2 = core.Utils.decompose_fn(num, s, true);
+                                        const fn1 = f2.a;
+                                        const fn2 = f2.b;
+                                        const aHasSin = fn1.containsFunction('sin');
+                                        const aHasCos = fn1.containsFunction('cos');
+                                        const bHasCos = fn2.containsFunction('cos');
+                                        const bHasSin = fn2.containsFunction('sin');
+                                        if (
+                                            f2.x.value === s &&
+                                            f2.x.isLinear() &&
+                                            !((aHasSin && bHasCos) || aHasCos || bHasSin)
+                                        ) {
+                                            retval = _.parse(
+                                                format(
+                                                    '(({1})*cos((sqrt(({2})*({3}))*({0}))/({2})))/({2})',
+                                                    t,
+                                                    f2.a,
+                                                    f.a,
+                                                    f.b
+                                                )
+                                            );
+                                        } else if (aHasSin && bHasCos) {
+                                            const sin = /** @type {NerdamerSymbolType} */ (fn1.findFunction?.('sin'));
+                                            const cos = /** @type {NerdamerSymbolType} */ (fn2.findFunction?.('cos'));
+                                            // Who has the s?
+                                            if (sin?.args?.[0].equals(cos?.args?.[0]) && !sin?.args?.[0].contains(s)) {
+                                                b = /** @type {NerdamerSymbolType} */ (
+                                                    _.divide(fn2, cos.toUnitMultiplier())
+                                                ).toString();
+                                                const c = sin.args[0].toString();
+                                                d = f.b;
+                                                const e = _.divide(fn1, sin.toUnitMultiplier());
+                                                exp =
+                                                    '(({1})*({2})*cos({3})*sin(sqrt({4})*({0})))/sqrt({4})+({1})*sin({3})*({5})*cos(sqrt({4})*({0}))';
+                                                retval = _.parse(format(exp, t, a, b, c, d, e));
+                                            }
+                                        }
+                                    } else {
+                                        retval = _.parse(
+                                            format(
+                                                '(({1})*sin((sqrt(({2})*({3}))*({0}))/({2})))/sqrt(({2})*({3}))',
+                                                t,
+                                                num,
+                                                f.a,
+                                                f.b
+                                            )
+                                        );
+                                    }
+                                }
+                            } else if (
+                                /** @type {FracType} */ (f.x.power).num &&
+                                /** @type {FracType} */ (f.x.power).num.equals(3) &&
+                                /** @type {FracType} */ (f.x.power).den.equals(2) &&
+                                num.contains('sqrt(pi)') &&
+                                !num.contains(s) &&
+                                num.isLinear()
+                            ) {
+                                b = /** @type {NerdamerSymbolType} */ (_.divide(num.clone(), _.parse('sqrt(pi)')));
+                                retval = _.parse(format('(2*({2})*sqrt({0}))/({1})', t, f.a, b, num));
+                            } else if (denP.equals(2) && f.x.power.equals(2)) {
+                                if (num.contains(s)) {
+                                    // Decompose the numerator to check value of s
+                                    f2 = core.Utils.decompose_fn(
+                                        /** @type {NerdamerSymbolType} */ (_.expand(num.clone())),
+                                        s,
+                                        true
+                                    );
+                                    if (f2.x.isComposite()) {
+                                        /** @type {DecomposeResultType[]} */
+                                        const sTerms = [];
+                                        // First collect the factors e.g. (a)(bx)(cx^2+d)
+                                        /** @type {DecomposeResultType[]} */
+                                        const symbols = /** @type {DecomposeResultType[]} */ (
+                                            num
+                                                .collectSymbols(x => {
+                                                    x = NerdamerSymbol.unwrapPARENS(x);
+                                                    /** @type {DecomposeResultType} */
+                                                    const decomp = core.Utils.decompose_fn(x, s, true);
+                                                    decomp.symbol = x;
+                                                    return decomp;
+                                                })
+                                                // Then sort them by power hightest to lowest
+                                                .sort((x1, x2) => {
+                                                    const p1 =
+                                                        /** @type {DecomposeResultType} */ (x1).x.value === s
+                                                            ? /** @type {number} */ (
+                                                                  /** @type {unknown} */ (
+                                                                      /** @type {DecomposeResultType} */ (x1).x.power
+                                                                  )
+                                                              )
+                                                            : 0;
+                                                    const p2 =
+                                                        /** @type {DecomposeResultType} */ (x2).x.value === s
+                                                            ? /** @type {number} */ (
+                                                                  /** @type {unknown} */ (
+                                                                      /** @type {DecomposeResultType} */ (x2).x.power
+                                                                  )
+                                                              )
+                                                            : 0;
+                                                    return p2 - p1;
+                                                })
+                                        );
+                                        a = new NerdamerSymbol(-1);
+                                        // Grab only the ones which have s
+                                        for (let i = 0; i < symbols.length; i++) {
+                                            const fc = symbols[i];
+                                            if (fc.x.value === s) {
+                                                sTerms.push(fc);
+                                            } else {
+                                                a = /** @type {NerdamerSymbolType} */ (_.multiply(a, fc.symbol));
+                                            }
+                                        }
+                                        // The following 2 assumptions are made
+                                        // 1. since the numerator was factored above then each s_term has a unique power
+                                        // 2. because the terms are sorted by descending powers then the first item
+                                        //    has the highest power
+                                        // We can now check for the next type s(s^2-a^2)/(s^2+a^2)^2
+                                        if (
+                                            sTerms[0].x.power.equals(2) &&
+                                            sTerms[1].x.power.equals(1) &&
+                                            sTerms[1].b.equals(0) &&
+                                            !sTerms[0].b.equals(0)
+                                        ) {
+                                            b = sTerms[0].a.negate();
+                                            exp =
+                                                '-(({1})*({2})*({5})*({0})*sin((sqrt(({4})*({5}))*({0}))/({4})))/' +
+                                                '(2*({4})^2*sqrt(({4})*({5})))-(({1})*({3})*({0})*sin((sqrt(({4})*({5}))*({0}))/({4})))' +
+                                                '/(2*({4})*sqrt(({4})*({5})))+(({1})*({2})*cos((sqrt(({4})*({5}))*({0}))/({4})))/({4})^2';
+                                            retval = _.parse(format(exp, t, a, b, sTerms[0].b, f.a, f.b));
+                                        }
+                                    } else if (f2.x.isLinear()) {
+                                        a = /** @type {NerdamerSymbolType} */ (_.divide(f2.a, new NerdamerSymbol(2)));
+                                        exp =
+                                            '(({1})*({0})*sin((sqrt(({2})*({3}))*({0}))/({2})))/(({2})*sqrt(({2})*({3})))';
+                                        retval = _.parse(format(exp, t, a, f.a, f.b));
+                                    } else if (f2.x.power.equals(2)) {
+                                        if (f2.b.equals(0)) {
+                                            a = /** @type {NerdamerSymbolType} */ (
+                                                _.divide(f2.a, new NerdamerSymbol(2))
+                                            );
+                                            exp =
+                                                '(({1})*sin((sqrt(({2})*({3}))*({0}))/({2})))/(({2})*sqrt(({2})*({3})))+(({1})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/({2})^2';
+                                            retval = _.parse(format(exp, t, a, f.a, f.b));
+                                        } else {
+                                            a = /** @type {NerdamerSymbolType} */ (
+                                                _.divide(f2.a, new NerdamerSymbol(2))
+                                            );
+                                            d = f2.b.negate();
+                                            exp =
+                                                '-((({2})*({4})-2*({1})*({3}))*sin((sqrt(({2})*({3}))*({0}))/({2})))/(2*({2})*({3})*sqrt(({2})*({3})))+' +
+                                                '(({4})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/(2*({2})*({3}))+(({1})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/({2})^2';
+                                            retval = _.parse(format(exp, t, a, f.a, f.b, d));
+                                        }
+                                    }
+                                } else {
+                                    a = /** @type {NerdamerSymbolType} */ (_.divide(num, new NerdamerSymbol(2)));
+                                    exp =
+                                        '(({1})*sin((sqrt(({2})*({3}))*({0}))/({2})))/(({3})*sqrt(({2})*({3})))-(({1})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/(({2})*({3}))';
+                                    retval = _.parse(format(exp, t, a, f.a, f.b));
+                                }
+                            } else if (symbol.isComposite()) {
+                                // 1/(s+1)^2
+                                if (denP.equals(2) && f.x.group === S) {
+                                    retval = _.parse(`(${m})*(${t})*e^(-(${f.b})*(${t}))`);
+                                } else {
+                                    retval = new NerdamerSymbol(0);
+
+                                    symbol = /** @type {NerdamerSymbolType} */ (
+                                        /** @type {PartFracSubModuleType} */ (Algebra.PartFrac).partfrac(
+                                            /** @type {NerdamerSymbolType} */ (_.expand(symbol)),
+                                            s_
+                                        )
+                                    );
+
+                                    symbol.each(x => {
+                                        retval = /** @type {NerdamerSymbolType} */ (
+                                            _.add(retval, __.LaPlace.inverse(x, s_, t))
+                                        );
+                                    }, true);
+                                }
+                            }
+                        }
+
+                        retval ||= _.symfunction('ilt', [inputSymbol, _.parse(String(s_)), _.parse(String(t))]);
+
+                        return /** @type {NerdamerSymbolType} */ (retval);
+                    },
+                    true
+                );
+            },
+        },
+        Statistics: {
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @returns {Record<string, number>}
+             */
+            frequencyMap(arr) {
+                /** @type {Record<string, number>} */
+                const map = {};
+                // Get the frequency map
+                for (let i = 0, l = arr.length; i < l; i++) {
+                    const e = arr[i];
+                    const key = e.toString();
+                    map[key] ||= 0; // Default it to zero
+                    map[key]++; // Increment
+                }
+                return map;
+            },
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @returns {NerdamerSymbolType[]}
+             */
+            sort(arr) {
+                return arr.sort((a, b) => {
+                    if (!a.isConstant() || !b.isConstant()) {
+                        _.error('Unable to sort! All values must be numeric');
+                    }
+                    return /** @type {number} */ (/** @type {unknown} */ (a.multiplier.subtract(b.multiplier)));
+                });
+            },
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @returns {NerdamerSymbolType}
+             */
+            count(arr) {
+                return new NerdamerSymbol(arr.length);
+            },
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @param {NerdamerSymbolType} [x_]
+             * @returns {NerdamerSymbolType}
+             */
+            sum(arr, x_) {
+                /** @type {NerdamerSymbolType} */
+                let sum = new NerdamerSymbol(0);
+                for (let i = 0, l = arr.length; i < l; i++) {
+                    const xi = arr[i].clone();
+                    if (x_) {
+                        sum = /** @type {NerdamerSymbolType} */ (
+                            _.add(_.pow(_.subtract(xi, x_.clone()), new NerdamerSymbol(2)), sum)
+                        );
+                    } else {
+                        sum = /** @type {NerdamerSymbolType} */ (_.add(xi, sum));
+                    }
+                }
+
+                return sum;
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            mean(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.mean(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                return /** @type {NerdamerSymbolType} */ (_.divide(__.Statistics.sum(args), __.Statistics.count(args)));
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            median(...args) {
+                /** @type {NerdamerSymbolType} */
+                let retval;
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.median(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                try {
+                    const sorted = __.Statistics.sort(args);
+                    const l = args.length;
+                    if (core.Utils.even(l)) {
+                        const mid = l / 2;
+                        retval = __.Statistics.mean(sorted[mid - 1], sorted[mid]);
+                    } else {
+                        retval = sorted[Math.floor(l / 2)];
+                    }
+                } catch (e) {
+                    if (/** @type {Error} */ (e).message === 'timeout') {
+                        throw e;
+                    }
+                    retval = _.symfunction('median', args);
+                }
+                return retval;
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            mode(...args) {
+                /** @type {NerdamerSymbolType} */
+                let retval;
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.mode(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+
+                const map = __.Statistics.frequencyMap(args);
+
+                // The mode of 1 item is that item as per issue #310 (verified by Happypig375).
+                if (core.Utils.keys(map).length === 1) {
+                    retval = args[0];
+                } else {
+                    // Invert by arraning them according to their frequency
+                    /** @type {Record<number, string | string[]>} */
+                    const inverse = {};
+                    for (const x in map) {
+                        if (!Object.hasOwn(map, x)) {
+                            continue;
+                        }
+                        const freq = map[x];
+                        // Check if it's in the inverse already
+                        if (freq in inverse) {
+                            const e = inverse[freq];
+                            // If it's already an array then just add it
+                            if (isArray(e)) {
+                                e.push(x);
+                            }
+                            // Convert it to and array
+                            else {
+                                inverse[freq] = [x, /** @type {string} */ (inverse[freq])];
+                            }
+                        } else {
+                            inverse[freq] = x;
+                        }
+                    }
+                    // The keys now represent the maxes. We want the max of those keys
+                    const keyNums = core.Utils.keys(inverse).map(k => Number(k));
+                    const maxKey = Math.max.apply(null, keyNums);
+                    const max = inverse[maxKey];
+                    // Check it's an array. If it is then map over the results and convert
+                    // them to NerdamerSymbol
+                    if (isArray(max)) {
+                        retval = _.symfunction(
+                            'mode',
+                            max.sort().map(v => _.parse(v))
+                        );
+                    } else {
+                        retval = _.parse(/** @type {string} */ (max));
+                    }
+                }
+
+                return retval;
+            },
+            /**
+             * @param {NerdamerSymbolType} k
+             * @param {NerdamerSymbolType[]} args
+             * @returns {NerdamerSymbolType}
+             */
+            gVariance(k, args) {
+                const x_ = __.Statistics.mean(...args);
+                const sum = __.Statistics.sum(args, x_);
+                return /** @type {NerdamerSymbolType} */ (_.multiply(k, sum));
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            variance(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.variance(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                const k = /** @type {NerdamerSymbolType} */ (
+                    _.divide(new NerdamerSymbol(1), __.Statistics.count(args))
+                );
+                return __.Statistics.gVariance(k, args);
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            sampleVariance(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.sampleVariance(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+
+                const k = /** @type {NerdamerSymbolType} */ (
+                    _.divide(new NerdamerSymbol(1), _.subtract(__.Statistics.count(args), new NerdamerSymbol(1)))
+                );
+                return __.Statistics.gVariance(k, args);
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            standardDeviation(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.standardDeviation(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                return /** @type {NerdamerSymbolType} */ (
+                    _.pow(__.Statistics.variance(...args), new NerdamerSymbol(1 / 2))
+                );
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            sampleStandardDeviation(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.sampleStandardDeviation(
+                        .../** @type {NerdamerSymbolType[]} */ (args[0].elements)
+                    );
+                }
+                return /** @type {NerdamerSymbolType} */ (
+                    _.pow(__.Statistics.sampleVariance(...args), new NerdamerSymbol(1 / 2))
+                );
+            },
+            /**
+             * @param {NerdamerSymbolType} x
+             * @param {NerdamerSymbolType} mean
+             * @param {NerdamerSymbolType} stdev
+             * @returns {NerdamerSymbolType}
+             */
+            zScore(x, mean, stdev) {
+                return /** @type {NerdamerSymbolType} */ (_.divide(_.subtract(x, mean), stdev));
+            },
+        },
+        Units: {
+            table: {
+                foot: '12 inch',
+                meter: '100 cm',
+                decimeter: '10 cm',
+            },
+        },
+    });
+
+    nerdamer.register([
+        {
+            name: 'laplace',
+            visible: true,
+            numargs: 3,
+            build() {
+                return __.LaPlace.transform;
+            },
+        },
+        {
+            name: 'ilt',
+            visible: true,
+            numargs: 3,
+            build() {
+                return __.LaPlace.inverse;
+            },
+        },
+        // Statistical
+        {
+            name: 'mean',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.mean;
+            },
+        },
+        {
+            name: 'median',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.median;
+            },
+        },
+        {
+            name: 'mode',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.mode;
+            },
+        },
+        {
+            name: 'smpvar',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.sampleVariance;
+            },
+        },
+        {
+            name: 'variance',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.variance;
+            },
+        },
+        {
+            name: 'smpstdev',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.sampleStandardDeviation;
+            },
+        },
+        {
+            name: 'stdev',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.standardDeviation;
+            },
+        },
+        {
+            name: 'zscore',
+            visible: true,
+            numargs: 3,
+            build() {
+                return __.Statistics.zScore;
+            },
+        },
+    ]);
+
+    // Link registered functions externally
+    nerdamer.updateAPI();
+})();
+
+// Added for all.min.js
+if (typeof module !== 'undefined') {
+    module.exports = nerdamer;
+}

tools/ui/src/lib/vendors/nerdamer-prime/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 together-science
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

tools/ui/src/lib/vendors/nerdamer-prime/all.js

@@ -0,0 +1,16 @@
+/*
+ * Author : Martin Donk
+ * Website : http://www.nerdamer.com
+ * Email : martin.r.donk@gmail.com
+ * Source : https://github.com/jiggzson/nerdamer
+ * Can be used to load all add-ons with one require
+ */
+
+const nerdamer = require('./nerdamer.core.js');
+require('./Algebra.js');
+require('./Calculus.js');
+require('./Solve.js');
+require('./Extra.js');
+
+// Export nerdamer
+module.exports = nerdamer;

tools/ui/src/lib/vendors/nerdamer-prime/constants.js

@@ -0,0 +1,261 @@
+/*
+ * Mathematical constants for nerdamer
+ * This file contains precomputed values and mathematical constants
+ * used throughout the library.
+ */
+
+/**
+ * Container of pregenerated prime numbers up to 2083 This array is used as a cache and can be extended at runtime by
+ * functions like generatePrimes()
+ */
+const PRIMES = [
+    2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109,
+    113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239,
+    241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379,
+    383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521,
+    523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661,
+    673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827,
+    829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991,
+    997, 1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069, 1087, 1091, 1093, 1097, 1103, 1109,
+    1117, 1123, 1129, 1151, 1153, 1163, 1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223, 1229, 1231, 1237, 1249, 1259,
+    1277, 1279, 1283, 1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373, 1381, 1399, 1409, 1423,
+    1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511, 1523, 1531, 1543,
+    1549, 1553, 1559, 1567, 1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657, 1663, 1667,
+    1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811, 1823,
+    1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987,
+    1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083,
+];
+
+/** Set representation of PRIMES for O(1) lookup This object is used as a cache and can be extended at runtime */
+/** @type {Record<number, boolean>} */
+const PRIMES_SET = {};
+for (const p of PRIMES) {
+    PRIMES_SET[p] = true;
+}
+
+/** High precision value of Pi (200 decimal places) Used for high-precision calculations */
+const LONG_PI =
+    '3.14159265358979323846264338327950288419716939937510582097494459230781640628620899862803482534211706798214' +
+    '808651328230664709384460955058223172535940812848111745028410270193852110555964462294895493038196';
+
+/** High precision value of Euler's number e (200 decimal places) Used for high-precision calculations */
+const LONG_E =
+    '2.718281828459045235360287471352662497757247093699959574966967627724076630353547594571382178525166427427466' +
+    '39193200305992181741359662904357290033429526059563073813232862794349076323382988075319525101901';
+
+/**
+ * Precomputed high-precision fraction values for the bigLog function. These are used for arbitrary-precision logarithm
+ * calculations. Each entry is a string representation of a high-precision rational number.
+ */
+const BIG_LOG_CACHE = [
+    '-253631954333118718762629409109262279926288908775918712466601196032/39970093576053625963957478139049824030906352922262642968060706375',
+    '0',
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+    '1953999166296955830935495158735359200362904181792947794529339487489730042568305997099959302322956898299616194932283060554261566410988618045107398092345476532371402134206635235570281738377188438407703089325315446371127042537576093536896282955524842632708645655481028161471313608974238110718242273935956977555610147714316158486553633871312187084618154014921190595222799283957140353/375191165084882521037046014569185165885459082629136124177286500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
+];
+
+if (typeof module !== 'undefined') {
+    module.exports = {
+        PRIMES,
+        PRIMES_SET,
+        LONG_PI,
+        LONG_E,
+        BIG_LOG_CACHE,
+    };
+}

tools/ui/src/lib/vendors/package.json

@@ -0,0 +1,3 @@
+{
+	"type": "commonjs"
+}

tools/ui/src/virtual-nerdamer.d.ts

@@ -0,0 +1,4 @@
+declare module 'virtual:nerdamer' {
+	const code: string;
+	export default code;
+}

tools/ui/tests/client/sandbox.service.svelte.test.ts

@@ -0,0 +1,59 @@
+import { beforeEach, describe, expect, it } from 'vitest';
+import { SandboxService } from '$lib/services/sandbox.service';
+import { SANDBOX_TOOL_NAME } from '$lib/constants';
+
+const run = (code: string, timeoutMs?: number) =>
+	SandboxService.executeTool(SANDBOX_TOOL_NAME, {
+		code,
+		...(timeoutMs !== undefined ? { timeout_ms: timeoutMs } : {})
+	});
+
+describe('sandbox service', () => {
+	beforeEach(async () => {
+		const { settingsStore } = await import('$lib/stores/settings.svelte');
+		settingsStore.config = {
+			...settingsStore.config,
+			symbolicMathEnabled: true
+		};
+	});
+
+	it('executes plain JavaScript', async () => {
+		const reply = await run('return 1 + 1;');
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> 2');
+	});
+
+	it('exposes nerdamer for symbolic computation', async () => {
+		const reply = await run("return nerdamer.diff('sin(x)/x', 'x').toString();");
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('cos(x)');
+	});
+
+	it('computes exact rational arithmetic', async () => {
+		const reply = await run("return nerdamer('1/3 + 1/6').toString();");
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> 1/2');
+	});
+
+	it('proves a polynomial identity symbolically', async () => {
+		const reply = await run(
+			"return nerdamer('expand((1+x*y)^3 - (1 + 3*x*y + 3*x^2*y^2 + x^3*y^3))').toString();"
+		);
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> 0');
+	});
+
+	it('blocks network egress in the worker via CSP', async () => {
+		const reply = await run(
+			"try { await fetch('https://example.com/'); return 'leaked'; } catch { return 'blocked'; }"
+		);
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> blocked');
+	});
+
+	it('enforces the timeout on runaway code', async () => {
+		const reply = await run('while (true) {}', 500);
+		expect(reply.isError).toBe(true);
+		expect(reply.content).toContain('timed out');
+	});
+});

tools/ui/tsconfig.json

@@ -25,7 +25,7 @@
 		".storybook/**/*.ts",
 		".storybook/**/*.svelte"
 	],
-	"exclude": ["src/lib/services/sandbox-worker.js"]
+	"exclude": ["src/lib/services/sandbox-worker.js", "src/lib/vendors/**"]
 	// Path aliases are handled by https://svelte.dev/docs/kit/configuration#alias
 	// except $lib which is handled by https://svelte.dev/docs/kit/configuration#files
 	//

tools/ui/vite.config.ts

@@ -9,6 +9,7 @@ import { storybookTest } from '@storybook/addon-vitest/vitest-plugin';
 import { splashScreenPlugin } from './scripts/vite-plugin-splash-screen';
 import { buildInfoPlugin } from './scripts/vite-plugin-build-info';
 import { relativizeBasePlugin } from './scripts/vite-plugin-relativize-base';
+import { nerdamerPlugin } from './scripts/vite-plugin-nerdamer';
 import { playwright } from '@vitest/browser-playwright';
 import { SVELTEKIT_PWA_OPTIONS } from './src/lib/constants/pwa';
 
@@ -46,6 +47,7 @@ export default defineConfig({
 		SvelteKitPWA(SVELTEKIT_PWA_OPTIONS),
 		splashScreenPlugin(),
 		buildInfoPlugin(),
+		nerdamerPlugin(),
 		relativizeBasePlugin()
 	],